{
  "name": "diogenes-neural-search-engine-mcp",
  "package": "neural-search-engine",
  "server_id": "io.github.stand-out-of-my-sun/diogenes-neural-search-engine",
  "tensorblock_profile_id": "github-stand-out-of-my-sun-diogenes-neural-search-engine-mcp-5ca59b2d",
  "version": "2.5.0",
  "description": "12 real-time neural search, grounded research & factual verification tools: neural search synthesizer, deep research dossier compiler, Boolean & semantic query expander, curated domain registry filter, breaking news recency & velocity tracker, factual consensus claim verifier, scholarly citation compiler (BibTeX/APA/IEEE), topic subgraph clusterer, bull/bear media sentiment oracle, expert source ranker, chronological timeline synthesizer, and competitive landscape matrix.",
  "protocol": "mcp-2024-11-05",
  "transport": "streamable-http",
  "category": "Security",
  "license": "MIT",
  "maintainer": "@stand-out-of-my-sun",
  "auth": {
    "type": "none",
    "accountless": true,
    "summary": "No auth required for free tier, MCP tool discovery, and ?demo=true previews (no account, no API key, no registration). Optional pay-per-request L402/x402/MPP headers for production execution.",
    "headers": {
      "L402": "Authorization: L402 <macaroon>:<preimage_hex>",
      "x402": "X-Payment-Signature: <solana_tx_signature>",
      "MPP": "X-Payment-TxHash: <base_evm_tx_hash>"
    }
  },
  "install_confidence": "high",
  "verification": {
    "status": "verified",
    "maintainer": "@stand-out-of-my-sun",
    "tensorblock_json": "https://diogenes-micro-gateway.corinth.workers.dev/.well-known/tensorblock.json",
    "glama_json": "https://diogenes-micro-gateway.corinth.workers.dev/.well-known/glama.json",
    "health_check": "https://diogenes-micro-gateway.corinth.workers.dev/api/v1/health"
  },
  "homepage": "https://diogenes-micro-gateway.corinth.workers.dev",
  "endpoints": {
    "mcp_streamable_http": "https://diogenes-micro-gateway.corinth.workers.dev/mcp",
    "health": "https://diogenes-micro-gateway.corinth.workers.dev/api/v1/health",
    "docs": "https://diogenes-micro-gateway.corinth.workers.dev/llms.txt",
    "support": "https://github.com/stand-out-of-my-sun/diogenes-micro-gateway/issues",
    "l402_lightning": "diogenesrecv0e236a@coinos.io",
    "x402_solana": "3vuU9jvaohMG6oPY7NVrUggcSJ4CuhcByxoQk68EC6hj",
    "mpp_base_evm": "0x0E97828055Ea1291e57dB32EBEfC6e7b3F7AcACb",
    "garlicoin_segwit": "grlc1qnd34hu5jrde5akkezy3c6y4vzw6xtntpxwdwxh",
    "garlicoin_classic": "GY1YBTTfgCEjMspTxJUed3FkvSasNgMQWu",
    "bsc_grlc": "0x7E720EE1675fd81aCfF14692E864eCFA17dc3e3e",
    "bsc_wgrlc_contract": "0x7283DfA2d8D7e277b148cc263B5d8Ae02f1076D3"
  },
  "tools": [
    {
      "name": "synthesize_neural_search",
      "title": "Neural Search Synthesizer & Multi-Domain Ranker",
      "description": "Executes multi-domain semantic search query expansion, relevance re-ranking, and extractive factual snippet bundling. Use when retrieving grounded web knowledge, evaluating search results across diverse domains, and retrieving cited answers; for building deep research briefings, use compile_research_dossier instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'search_query' ('search_mode' defaults to 'hybrid_semantic_keyword'), supports '?demo=true' or L402 (25 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "search_query": {
            "type": "string",
            "description": "Primary search query, factual question, or technical topic to search."
          },
          "search_mode": {
            "type": "string",
            "description": "Information retrieval strategy: hybrid semantic keyword, strict fact retrieval, or deep research.",
            "enum": [
              "hybrid_semantic_keyword",
              "strict_fact_retrieval",
              "deep_research"
            ]
          },
          "max_results": {
            "type": "integer",
            "description": "Maximum number of ranked result snippets to synthesize."
          }
        },
        "required": [
          "search_query"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for synthesize_neural_search.",
        "properties": {
          "query": {
            "type": "string",
            "description": "Normalized search query analyzed by the search planner."
          },
          "results": {
            "type": "array",
            "description": "Array of ranked search snippets with title, url, snippet, and score."
          },
          "search_strategy": {
            "type": "string",
            "description": "Retrieval strategy executed by the neural search planner."
          },
          "relevance_score": {
            "type": "number",
            "description": "Aggregate query-document semantic alignment score."
          }
        },
        "required": [
          "query",
          "results",
          "search_strategy",
          "relevance_score"
        ]
      },
      "annotations": {
        "title": "Neural Search Synthesizer & Multi-Domain Ranker",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "compile_research_dossier",
      "title": "Deep Research Dossier & Citation Intelligence Brief",
      "description": "Compiles multi-source factual research briefs with verified citations, confidence intervals, and key analytical takeaways. Use when synthesizing comprehensive intelligence briefings on complex technical, scientific, or market developments; for rapid query search snippets, use synthesize_neural_search instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'research_topic' ('depth_level' defaults to 'executive_brief'), supports '?demo=true' or L402 (35 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "research_topic": {
            "type": "string",
            "description": "Complex subject, market sector, emerging technology, or policy topic to research."
          },
          "depth_level": {
            "type": "string",
            "description": "Research dossier rigor: executive brief, deep investigation, or technical whitepaper.",
            "enum": [
              "executive_brief",
              "deep_investigation",
              "technical_whitepaper"
            ]
          },
          "target_citations": {
            "type": "integer",
            "description": "Target number of primary citations to identify and verify."
          }
        },
        "required": [
          "research_topic"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for compile_research_dossier.",
        "properties": {
          "title": {
            "type": "string",
            "description": "Title of the compiled research dossier."
          },
          "executive_summary": {
            "type": "string",
            "description": "High-level analytical summary of primary findings and consensus."
          },
          "key_findings": {
            "type": "array",
            "description": "Structured list of core factual insights with supporting context."
          },
          "citations": {
            "type": "array",
            "description": "Verified source citations backing the analytical findings."
          },
          "confidence_index": {
            "type": "number",
            "description": "Epistemic confidence index between 0.0 and 1.0 based on source consensus."
          }
        },
        "required": [
          "title",
          "executive_summary",
          "key_findings",
          "citations",
          "confidence_index"
        ]
      },
      "annotations": {
        "title": "Deep Research Dossier & Citation Intelligence Brief",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "expand_search_queries",
      "title": "High-Entropy Boolean & Semantic Search Query Expander",
      "description": "Generates targeted Boolean queries, semantic synonym variations, and sub-question expansions from an ambiguous prompt. Use when overcoming query vocabulary mismatches and expanding search recall across academic, code, or patent databases; for scoping queries to domain categories, use filter_domain_intelligence instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'base_query' ('expansion_strategy' defaults to 'boolean_operators'), supports '?demo=true' or L402 (10 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "base_query": {
            "type": "string",
            "description": "Initial search prompt or ambiguous question requiring semantic query expansion."
          },
          "expansion_strategy": {
            "type": "string",
            "description": "Query expansion method: Boolean operators, semantic synonyms, or cross-domain questions.",
            "enum": [
              "boolean_operators",
              "semantic_synonyms",
              "cross_domain_questions"
            ]
          },
          "target_count": {
            "type": "integer",
            "description": "Target count of query variations to synthesize."
          }
        },
        "required": [
          "base_query"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for expand_search_queries.",
        "properties": {
          "original_query": {
            "type": "string",
            "description": "Original user input query before expansion."
          },
          "expanded_queries": {
            "type": "array",
            "description": "Array of synthesized search strings optimized for search engine indexing."
          },
          "intent_classification": {
            "type": "string",
            "description": "Identified query intent (e.g., informational, transactional, technical_navigational)."
          }
        },
        "required": [
          "original_query",
          "expanded_queries",
          "intent_classification"
        ]
      },
      "annotations": {
        "title": "High-Entropy Boolean & Semantic Search Query Expander",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "filter_domain_intelligence",
      "title": "Curated Domain Registry Scope & Source Authority Filter",
      "description": "Scopes search execution and filters results strictly across verified high-reputation domain registries and regulatory bodies. Use when restricting search discovery to verified academic papers, SEC filings, official documentation, or legal statutes; for checking specific factual claims, use verify_factual_claims instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'query' ('domain_tier' defaults to 'academic_and_research'), supports '?demo=true' or L402 (10 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": {
            "type": "string",
            "description": "Topic or query to execute across specialized domain registries."
          },
          "domain_tier": {
            "type": "string",
            "description": "Curated domain authority tier: academic and research, regulatory and legal, tech docs, or financial.",
            "enum": [
              "academic_and_research",
              "regulatory_and_legal",
              "tech_documentation",
              "financial_and_sec"
            ]
          },
          "time_horizon": {
            "type": "string",
            "description": "Optional recency filter window (e.g., '1y', '5y', 'all_time')."
          }
        },
        "required": [
          "query"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for filter_domain_intelligence.",
        "properties": {
          "domain_category": {
            "type": "string",
            "description": "Domain tier authority classification applied."
          },
          "ranked_sources": {
            "type": "array",
            "description": "Curated list of authoritative domain sources and matching resource URLs."
          },
          "whitelist_applied": {
            "type": "array",
            "description": "List of domain regex patterns and whitelisted hosts enforced."
          }
        },
        "required": [
          "domain_category",
          "ranked_sources",
          "whitelist_applied"
        ]
      },
      "annotations": {
        "title": "Curated Domain Registry Scope & Source Authority Filter",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "analyze_news_recency",
      "title": "Real-Time News Recency, Trending Story Clusters & Velocity Tracker",
      "description": "Tracks breaking news feeds, clusters trending story cycles, and calculates story publication velocity over recent timeframes. Use when analyzing breaking market catalysts, emerging controversies, or ongoing product announcements; for evaluating sentiment polarity across media, use analyze_media_sentiment instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'topic' ('recency_window' defaults to 'past_24_hours'), supports '?demo=true' or L402 (20 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "topic": {
            "type": "string",
            "description": "Subject, ticker symbol, brand, or public event to scan for breaking news developments."
          },
          "recency_window": {
            "type": "string",
            "description": "News timestamp search window: past hour, past 24 hours, past 7 days, or past 30 days.",
            "enum": [
              "past_hour",
              "past_24_hours",
              "past_7_days",
              "past_30_days"
            ]
          },
          "sentiment_cluster": {
            "type": "boolean",
            "description": "Whether to calculate sentiment direction across news story clusters."
          }
        },
        "required": [
          "topic"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for analyze_news_recency.",
        "properties": {
          "topic": {
            "type": "string",
            "description": "Monitored news topic or subject entity."
          },
          "story_clusters": {
            "type": "array",
            "description": "Deduplicated clusters of related news coverage with source attribution."
          },
          "temporal_velocity": {
            "type": "string",
            "description": "Rate of new article publication (e.g., accelerating, steady, tapering)."
          },
          "top_headlines": {
            "type": "array",
            "description": "List of most prominent headline items with timestamps and publishers."
          }
        },
        "required": [
          "topic",
          "story_clusters",
          "temporal_velocity",
          "top_headlines"
        ]
      },
      "annotations": {
        "title": "Real-Time News Recency, Trending Story Clusters & Velocity Tracker",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "verify_factual_claims",
      "title": "Fact Verification & Consensus Evidence Evaluator",
      "description": "Audits factual statements against authoritative consensus sources and assigns truthfulness scores with counter-evidence. Use when checking disputed claims, detecting AI hallucinations, or validating quotes before citation; for formatting academic citations, use extract_scholarly_citations instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'claim_statement' ('verification_rigor' defaults to 'multi_source_consensus'), supports '?demo=true' or L402 (20 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "claim_statement": {
            "type": "string",
            "description": "Factual assertion, statistic, or quote requiring independent source verification."
          },
          "verification_rigor": {
            "type": "string",
            "description": "Fact verification method: multi-source consensus, official records only, or probabilistic check.",
            "enum": [
              "multi_source_consensus",
              "official_records_only",
              "probabilistic_check"
            ]
          },
          "context_domain": {
            "type": "string",
            "description": "Optional domain context such as economics, medicine, history, or computer science."
          }
        },
        "required": [
          "claim_statement"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for verify_factual_claims.",
        "properties": {
          "verdict": {
            "type": "string",
            "description": "Factual evaluation verdict (VERIFIED_TRUE, DISPUTED, REFUTED_FALSE, UNVERIFIABLE)."
          },
          "truth_confidence": {
            "type": "number",
            "description": "Confidence percentage score between 0.0 and 1.0 in the verdict."
          },
          "supporting_evidence": {
            "type": "array",
            "description": "Authoritative statements and sources supporting the claim."
          },
          "refuting_evidence": {
            "type": "array",
            "description": "Contradictory findings or counter-evidence disproving the claim."
          }
        },
        "required": [
          "verdict",
          "truth_confidence",
          "supporting_evidence",
          "refuting_evidence"
        ]
      },
      "annotations": {
        "title": "Fact Verification & Consensus Evidence Evaluator",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "extract_scholarly_citations",
      "title": "Scholarly Citation Compiler (BibTeX, APA, IEEE, MLA) & DOI Resolver",
      "description": "Formats academic paper metadata and DOI identifiers into standard scholarly citation formats (BibTeX, APA, IEEE, MLA). Use when generating bibliographies for research papers, validating journal references, or resolving DOI identifiers; for structuring research timelines, use summarize_temporal_timeline instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'source_content_or_doi' ('citation_style' defaults to 'bibtex_format'), supports '?demo=true' or L402 (10 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "source_content_or_doi": {
            "type": "string",
            "description": "DOI string, arXiv ID, or raw paper title with author list to format into citation."
          },
          "citation_style": {
            "type": "string",
            "description": "Bibliographic citation standard: BibTeX format, APA 7th edition, IEEE standard, or MLA 9th edition.",
            "enum": [
              "bibtex_format",
              "apa_7th_edition",
              "ieee_standard",
              "mla_9th_edition"
            ]
          },
          "include_abstract": {
            "type": "boolean",
            "description": "Whether to include publication abstract in structured metadata."
          }
        },
        "required": [
          "source_content_or_doi"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for extract_scholarly_citations.",
        "properties": {
          "formatted_citation": {
            "type": "string",
            "description": "Formatted bibliographic reference string adhering to the chosen citation standard."
          },
          "citation_style": {
            "type": "string",
            "description": "Citation specification name applied to the formatted reference."
          },
          "metadata": {
            "type": "object",
            "description": "Parsed bibliographic fields including title, authors, year, journal, and doi."
          }
        },
        "required": [
          "formatted_citation",
          "citation_style",
          "metadata"
        ]
      },
      "annotations": {
        "title": "Scholarly Citation Compiler (BibTeX, APA, IEEE, MLA) & DOI Resolver",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "cluster_topic_subgraphs",
      "title": "Semantic Topic Clustering & Subgraph Taxonomy Synthesizer",
      "description": "Discovers latent thematic clusters, subtopic branches, and conceptual relationships across document corpora. Use when structuring broad search results into distinct thematic silos or generating table of contents taxonomies; for tracking chronological milestones, use summarize_temporal_timeline instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'documents_or_queries' ('clustering_method' defaults to 'tf_idf_semantic_graph'), supports '?demo=true' or L402 (15 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "documents_or_queries": {
            "type": "string",
            "description": "Newline-separated list or JSON array of text snippets, queries, or article abstracts."
          },
          "clustering_method": {
            "type": "string",
            "description": "Thematic clustering algorithm: TF-IDF semantic graph, centroid k-means, or hierarchical taxonomy.",
            "enum": [
              "tf_idf_semantic_graph",
              "centroid_kmeans",
              "hierarchical_taxonomy"
            ]
          },
          "max_clusters": {
            "type": "integer",
            "description": "Maximum number of thematic cluster groups to partition data into."
          }
        },
        "required": [
          "documents_or_queries"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for cluster_topic_subgraphs.",
        "properties": {
          "clusters": {
            "type": "array",
            "description": "List of identified topic clusters with representative labels and member documents."
          },
          "total_nodes": {
            "type": "integer",
            "description": "Total number of items successfully clustered into the taxonomy."
          },
          "modularity_score": {
            "type": "number",
            "description": "Clustering partition modularity metric indicating distinctness of clusters."
          }
        },
        "required": [
          "clusters",
          "total_nodes",
          "modularity_score"
        ]
      },
      "annotations": {
        "title": "Semantic Topic Clustering & Subgraph Taxonomy Synthesizer",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "analyze_media_sentiment",
      "title": "Multi-Source Media Sentiment & Bull/Bear Polarity Oracle",
      "description": "Evaluates brand, market, or topic sentiment across news headlines, discussions, and articles with bull/bear polarity scores. Use when assessing market sentiment, social brand perception, or controversy exposure on specific entities; for compiling competitive market comparisons, use compile_competitive_landscape instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text_or_headlines' ('sentiment_domain' defaults to 'general_news'), supports '?demo=true' or L402 (15 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "text_or_headlines": {
            "type": "string",
            "description": "Collection of headlines, commentary strings, or editorial paragraphs to evaluate."
          },
          "sentiment_domain": {
            "type": "string",
            "description": "Lexicon domain weighting: general news, financial markets, or developer ecosystem.",
            "enum": [
              "general_news",
              "financial_markets",
              "developer_ecosystem"
            ]
          },
          "entity_filter": {
            "type": "string",
            "description": "Optional specific brand, company, or coin name to isolate sentiment around."
          }
        },
        "required": [
          "text_or_headlines"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for analyze_media_sentiment.",
        "properties": {
          "aggregate_score": {
            "type": "number",
            "description": "Continuous sentiment polarity score between -1.0 (bearish/negative) and +1.0 (bullish/positive)."
          },
          "sentiment_tier": {
            "type": "string",
            "description": "Categorical sentiment classification (VERY_BULLISH, MODERATELY_POSITIVE, NEUTRAL, NEGATIVE, PANIC)."
          },
          "bull_bear_ratio": {
            "type": "number",
            "description": "Ratio of positive signal markers to negative signal markers."
          },
          "emotional_signals": {
            "type": "object",
            "description": "Sub-metrics for optimism, fear/uncertainty, and enthusiasm."
          }
        },
        "required": [
          "aggregate_score",
          "sentiment_tier",
          "bull_bear_ratio",
          "emotional_signals"
        ]
      },
      "annotations": {
        "title": "Multi-Source Media Sentiment & Bull/Bear Polarity Oracle",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "rank_expert_sources",
      "title": "Domain Expert Authority Ranking & Primary Source Discovery",
      "description": "Identifies and ranks top domain authorities, leading researchers, and primary documentation repositories for technical topics. Use when finding primary source experts to cite or discovering foundational documentation for specialized fields; for ranking general search query snippets, use synthesize_neural_search instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'subject_area' ('authority_metric' defaults to 'pagerank_and_domain_authority'), supports '?demo=true' or L402 (10 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "subject_area": {
            "type": "string",
            "description": "Academic field, open source framework, protocol, or technical discipline to explore."
          },
          "authority_metric": {
            "type": "string",
            "description": "Reputation evaluation standard: PageRank and domain authority, citation h-index, or code repo stars.",
            "enum": [
              "pagerank_and_domain_authority",
              "citation_h_index",
              "code_repository_stars"
            ]
          },
          "max_experts": {
            "type": "integer",
            "description": "Maximum number of top domain authorities to return in the ranking."
          }
        },
        "required": [
          "subject_area"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for rank_expert_sources.",
        "properties": {
          "subject": {
            "type": "string",
            "description": "Analyzed discipline or technical topic."
          },
          "top_authorities": {
            "type": "array",
            "description": "List of top-ranked domain experts, core maintainers, or key institutions."
          },
          "recommended_primary_sources": {
            "type": "array",
            "description": "Curated list of authoritative repositories, RFCs, and documentation hubs."
          }
        },
        "required": [
          "subject",
          "top_authorities",
          "recommended_primary_sources"
        ]
      },
      "annotations": {
        "title": "Domain Expert Authority Ranking & Primary Source Discovery",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "summarize_temporal_timeline",
      "title": "Chronological Event Timeline Compiler & Milestone Synthesizer",
      "description": "Synthesizes chronological milestone timelines from search results, news archives, and historical documents. Use when reconstructing historical events, tracking company histories, or mapping regulatory progression; for tracking real-time breaking news in the past 24 hours, use analyze_news_recency instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'event_corpus' ('timeline_resolution' defaults to 'chronological_milestones'), supports '?demo=true' or L402 (15 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "event_corpus": {
            "type": "string",
            "description": "Unstructured text corpus containing dated events, news reports, or historical accounts."
          },
          "timeline_resolution": {
            "type": "string",
            "description": "Temporal event grouping: chronological milestones, daily chronicle, or weekly epochs.",
            "enum": [
              "chronological_milestones",
              "daily_chronicle",
              "weekly_epochs"
            ]
          },
          "ascending_order": {
            "type": "boolean",
            "description": "Whether to sort timeline from oldest to newest (true) or reverse chronological (false)."
          }
        },
        "required": [
          "event_corpus"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for summarize_temporal_timeline.",
        "properties": {
          "timeline_events": {
            "type": "array",
            "description": "Ordered sequence of date-stamped milestone entries with descriptions."
          },
          "date_range": {
            "type": "object",
            "description": "Start date and end date spanning the compiled chronological timeline."
          },
          "turning_points": {
            "type": "array",
            "description": "Key pivot points or inflection events identified in the historical narrative."
          }
        },
        "required": [
          "timeline_events",
          "date_range",
          "turning_points"
        ]
      },
      "annotations": {
        "title": "Chronological Event Timeline Compiler & Milestone Synthesizer",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    },
    {
      "name": "compile_competitive_landscape",
      "title": "Competitor Intelligence, Feature Matrix & Moat Analyzer",
      "description": "Compiles multi-competitor comparison matrices, pricing tiers, feature coverage, and strategic moat evaluations. Use when analyzing market landscapes, evaluating pricing parity, or conducting diligence on competing product offerings; for compiling narrative research briefs, use compile_research_dossier instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'target_entity_or_market' ('analysis_framework' defaults to 'feature_and_pricing_matrix'), supports '?demo=true' or L402 (25 sats), and returns HTTP 400 if invalid.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "target_entity_or_market": {
            "type": "string",
            "description": "Industry sector, product category, or target company to benchmark against competitors."
          },
          "analysis_framework": {
            "type": "string",
            "description": "Benchmarking framework: feature and pricing matrix, SWOT competitive moat, or market share quadrant.",
            "enum": [
              "feature_and_pricing_matrix",
              "swot_competitive_moat",
              "market_share_quadrant"
            ]
          },
          "max_competitors": {
            "type": "integer",
            "description": "Maximum number of market competitors to profile and contrast."
          }
        },
        "required": [
          "target_entity_or_market"
        ]
      },
      "outputSchema": {
        "type": "object",
        "description": "Deterministic execution response schema for compile_competitive_landscape.",
        "properties": {
          "market_sector": {
            "type": "string",
            "description": "Standardized market sector or product category analyzed."
          },
          "competitor_matrix": {
            "type": "array",
            "description": "Structured matrix comparing market players, target customers, and offerings."
          },
          "pricing_comparison": {
            "type": "array",
            "description": "Benchmarked pricing models, free tiers, and entry price points across players."
          },
          "strategic_advantages": {
            "type": "array",
            "description": "Identified competitive moats, network effects, and differentiation factors."
          }
        },
        "required": [
          "market_sector",
          "competitor_matrix",
          "pricing_comparison",
          "strategic_advantages"
        ]
      },
      "annotations": {
        "title": "Competitor Intelligence, Feature Matrix & Moat Analyzer",
        "readOnlyHint": true,
        "destructiveHint": false,
        "idempotentHint": true,
        "openWorldHint": false
      }
    }
  ]
}