Caselaw

{
  "id": "aix_a92f8c3b8b",
  "name": "Caselaw",
  "operator": "(unclaimed - source: pulsemcp · publisher: github.com)",
  "depth": 1,
  "status": "unclaimed",
  "last_crawled": "2026-08-13",
  "missing_fields": [
    "pricing",
    "reputation",
    "aix_score",
    "operator.identity"
  ],
  "skills": [
    "citation-check"
  ],
  "protocols": {
    "mcp": "https://caselaw.patent-space.dev/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": null,
  "aix_score": null,
  "verification": {
    "identity": "none",
    "health": "unreachable",
    "pricing": "unknown",
    "last_check": "2026-08-13T06:01:09.132Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://www.pulsemcp.com/servers/agentic-governance-caselaw"
    },
    {
      "label": "repository",
      "url": "https://github.com/agentic-governance/caselaw-mcp"
    }
  ],
  "avatar": "https://github.com/agentic-governance.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/agentic-governance"
    }
  ],
  "public_stats": {
    "gh_stars": 1
  },
  "unreachable": false,
  "profile": {
    "mcp_server": "legal-mcp",
    "mcp_version": "3.1.1",
    "tool_count": 40,
    "tools": [
      {
        "name": "tool_search_case_law",
        "description": "Search case law across jurisdiction/topic/keywords/year range."
      },
      {
        "name": "tool_search_statute",
        "description": "Search statute texts by jurisdiction and optional filters."
      },
      {
        "name": "tool_generate_legal_draft",
        "description": "Generate legal markdown draft for common document types."
      },
      {
        "name": "tool_estimate_legal_risk",
        "description": "Estimate legal risk for a provided situation."
      },
      {
        "name": "search_cases",
        "description": "任意の管轄で判例をフルテキスト付きで検索する。\n\nArgs:\n    query: 検索クエリ（事案の記述、キーワード、当事者名など）\n    jurisdiction: 管轄（US, GB, EU, JP, AU, CA, DE, FR, IN, BR, KR, etc.）\n    max_results: 最大取得件数\n    legal_area: 法分野フィルタ（contract, tort, ip, criminal, etc.）\n\nReturns:\n    dict: {\n        \"results\": [{\"case_id\": str, \"title\": str, \"text\": str,\n                    \"date\": str, \"court\": str, \"source_url\": str}],\n        \"count\": int,\n        \"jurisdiction\": str\n    }"
      },
      {
        "name": "search_cases_global",
        "description": "複数管轄を横断して判例を検索する。\n\nArgs:\n    query: 検索クエリ\n    jurisdictions: 検索対象管轄リスト（デフォルト: [\"US\", \"GB\", \"EU\", \"JP\", \"AU\"]）\n    max_results_per_jurisdiction: 各管轄での最大取得件数\n\nReturns:\n    dict: {\n        \"results\": {<jurisdiction>: [cases...]},\n        \"total_count\": int,\n        \"jurisdictions_searched\": list[str]\n    }"
      },
      {
        "name": "get_case_detail",
        "description": "特定の判例のフルテキストと要旨を取得する。\n\nArgs:\n    case_id: 判例ID（各管轄固有のID）\n    jurisdiction: 管轄コード\n\nReturns:\n    dict: {\n        \"case_id\": str,\n        \"title\": str,\n        \"full_text\": str,\n        \"summary\": str,\n        \"date\": str,\n        \"court\": str,\n        \"parties\": list[str],\n        \"citations\": list[str],\n        \"source_url\": str\n    }"
      },
      {
        "name": "find_similar_cases",
        "description": "事案の記述から類似判例を検索する。\n\nArgs:\n    text: 事案の記述（事実関係、争点、法的問題など）\n    jurisdiction: 管轄（\"ALL\"で全管轄を検索）\n    max_results: 最大取得件数\n\nReturns:\n    dict: {\n        \"similar_cases\": [{\"case_id\": str, \"title\": str, \"relevance\": float,\n                          \"text\": str, \"jurisdiction\": str}],\n        \"query_text\": str,\n        \"search_jurisdiction\": str\n    }"
      },
      {
        "name": "analyze_legal_trend",
        "description": "特定の法分野における判例トレンドを分析する。\n\nArgs:\n    legal_area: 法分野（contract, tort, ip, criminal, administrative, etc.）\n    jurisdiction: 管轄コード\n    date_from: 開始日（YYYY-MM-DD形式）\n    date_to: 終了日（YYYY-MM-DD形式）\n\nReturns:\n    dict: {\n        \"trend_summary\": str,\n        \"case_count\": int,\n        \"time_period\": {\"from\": str, \"to\": str},\n        \"key_developments\": list[str],\n        \"landmark_cases\": list[dict],\n        \"legal_area\": str,\n        \"jurisdiction\": str\n    }"
      },
      {
        "name": "tool_search_ip_stats",
        "description": "Search IP statistics (patents, trademarks, GII rankings) by jurisdiction and indicator."
      },
      {
        "name": "tool_ip_dispute_search",
        "description": "Search IP disputes and enforcement data (UDRP/Section337/EPO Opposition/PTAB/Special301/CBP)."
      },
      {
        "name": "tool_ip_enforcement_profile",
        "description": "Return integrated IP enforcement profile (Special 301 + seizures + Notorious Markets)."
      },
      {
        "name": "tool_ip_dispute_forum_comparison",
        "description": "Compare IP dispute forum volumes: UDRP vs Section 337 vs EPO Opposition vs PTAB."
      },
      {
        "name": "tool_ip_list_dispute_indicators",
        "description": "List all available IP dispute and enforcement indicators by source."
      },
      {
        "name": "extract_case_metadata",
        "description": "Extract structured metadata from a judgment / decision / UDRP / opposition text.\n\nWorkflow:\n  1. Call with text + hint_source → get partially-extracted JSON schema (dates,\n     case numbers, damages, provisions extracted by regex).\n  2. The calling LLM fills in the remaining fields (parties, liability,\n     technical_sector, notes, etc.) using its language understanding.\n  3. Call again with llm_filled=<your completed dict> to normalize dates,\n     resolve entity names, validate schema, and get the final clean JSON.\n\nArgs:\n  text:        Raw judgment text (any language).\n  hint_source: Source hint for procedure/ip_field defaults.\n               Values: \"PTAB\", \"ITC337\", \"EPO-Opposition\", \"WIPO-UDRP\",\n                       \"UPC\", \"JP-Court\", \"CN-Court\", \"CourtListener\", etc.\n  llm_filled:  If provided, skip extraction and normalize/validate this dict instead.\n\nReturns:\n  dict with:\n    \"schema\"      – the (partial or complete) metadata JSON\n    \"valid\"       – True if schema passes validation\n    \"issues\"      – list of validation issues (empty if valid)\n    \"instructions\"– guidance for the LLM on which fields still need filling"
      },
      {
        "name": "case_metadata_schema",
        "description": "Return the empty case metadata schema template.\nUse this to understand the required JSON structure before filling manually."
      },
      {
        "name": "ip_entity_resolve",
        "description": "Resolve a company/organization name to its canonical form.\nReturns canonical name, ID, country, known aliases, and confidence score.\nUseful before running ip_entity_profile to verify the correct entity.\nAccepts any language and format (English, Japanese, Chinese, etc.)."
      },
      {
        "name": "ip_entity_profile",
        "description": "Cross-source IP activity profile for a company/organization.\nSearches available data sources for the entity's IP footprint.\n\nReturns:\n  - Resolved canonical entity info\n  - Patent filing activity (PCT, national offices if data available)\n  - SEP/FRAND involvement\n  - Dispute involvement (PTAB, ITC, EPO opposition, UPC)\n  - Enforcement issues (USTR 301 mention if any)\n  - Technology trends"
      },
      {
        "name": "ip_entity_search",
        "description": "Search the entity registry by name, country, type, or industry.\nentity_type: \"corporation\", \"npe\", \"university\", \"government\", \"individual\"\nindustry: \"telecommunications\", \"semiconductors\", \"pharmaceuticals\", \"automotive\", etc."
      },
      {
        "name": "ip_events_detect",
        "description": "Detect changes and notable events across all IP data sources.\nCompares current values against previous snapshots.\n\nArgs:\n  severity: Filter by \"critical\", \"warning\", or \"info\" (None = all)\n  source_id: Filter by specific data source ID\n  country_code: Filter by country (e.g., \"US\", \"CN\", \"DE\")\n  limit: Max events to return\n\nReturns list of detected events sorted by severity then recency.\nEmoji indicators: 🔴 CRITICAL, 🟡 WARNING, 🔵 INFO"
      },
      {
        "name": "ip_events_snapshot",
        "description": "Take a snapshot of current IP statistics for future change detection.\nRun periodically to enable ip_events_detect to find changes.\n\nArgs:\n  source_id: Snapshot specific source only (None = all sources)\n\nReturns summary: number of indicators captured, timestamp."
      },
      {
        "name": "ip_events_history",
        "description": "View previously detected and recorded IP events.\n\nArgs:\n  days_back: Look back this many days (default 30)\n  severity: Filter by \"critical\", \"warning\", or \"info\"\n  country_code: Filter by country\n  limit: Max events to return"
      },
      {
        "name": "assess_forum_and_risk",
        "description": "Generate a cross-border IP enforcement playbook with candidate forums and recommended strategy."
      },
      {
        "name": "get_ip_dispute_profile",
        "description": "Get IP litigation analytics for specified entities. Returns case counts, win/loss ratio, key counterparties."
      },
      {
        "name": "tool_analyze_party_performance",
        "description": "Analyze litigation party performance with quality-controlled statistical analysis.\n\nReturns standardized analytics output including:\n- Win rate with Bayesian confidence intervals\n- Benchmark comparison vs sector average\n- Temporal trend analysis (improving/declining/stable)\n- Geographic segmentation & weakest segment identification\n- Statistical outlier detection (3σ threshold)\n- Red flags and priority-ranked strategic recommendations\n\nArgs:\n    entity: Party name to analyze (e.g., \"Cloudflare\", \"Google\")\n    sector: Industry sector for benchmarking, one of:\n        - \"cdn_provider\" (default): CDN/hosting providers\n        - \"streaming_platform\": Video/audio streaming services\n        - \"ecommerce_marketplace\": E-commerce platforms\n    cases: Optional list of case dicts. If None, searches case law database.\n        Each case should have: case_id, case_name, parties, defendants,\n        jurisdiction, year, result.\n\nReturns:\n    dict with AnalyticsOutput schema (15 mandatory fields):\n    - metric_value: float (e.g., 0.68 for 68% win rate)\n    - metric_name: str (e.g., \"defendant_win_rate\")\n    - n_cases: int (sample size)\n    - confidence_interval: [float, float] (95% Bayesian CI)\n    - sample_quality: \"reliable\" | \"limited\" | \"unreliable\"\n    - sector_benchmark: float (industry average)\n    - benchmark_delta: float (difference from benchmark)\n    - timeline: [[year, rate], ...] (temporal trend data)\n    - trend: \"improving\" | \"declining\" | \"stable\"\n    - trend_rate: float | null (percentage change per year)\n    - segments: {jurisdiction: rate, ...}\n    - weakest_segment: [jurisdiction, rate] | null\n    - outliers: [str, ...] (3σ outliers)\n    - red_flags: [str, ...] (warnings)\n    - supporting_cases: [case_id, ...]\n\nExample:\n    >>> tool_analyze_party_performance(\"Cloudflare\", \"cdn_provider\")\n    {\n      \"metric_value\": 0.68,\n      \"n_cases\": 73,\n      \"sample_quality\": \"reliable\",\n      \"confidence_interval\": [0.57, 0.77],\n      \"sector_benchmark\": 0.55,\n      \"benchmark_delta\": 0.13,\n      \"trend\": \"declining\",\n      \"weakest_segment\": [\"France\", 0.25],\n      \"red_flags\": [\"Critical weakness in France: 25% vs overall 68%\"],\n      ...\n    }\n\nQuality Guarantees:\n- ALL numbers computed in Python (zero hallucination risk)\n- Mandatory confidence interval disclosure\n- Mandatory benchmark comparison\n- Mandatory sample size disclosure\n- Sample quality assessment (reliable: N≥30, limited: N≥10, unreliable: N<10)"
      }
    ],
    "profiled_at": "2026-08-13T05:53:40.323Z"
  }
}