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gapup-mcp

by io.github.getgapup

court_filings_multi

Read-only

Aggregate court filings, judgments and litigation records for a company or individual across five major legal jurisdictions: US (CourtListener / PACER), UK (National Archives — EWHC/EWCA/UKSC/UKUT), EU (ECHR HUDOC — European Court of Human Rights), France (Légifrance / Cour de cassation) and Germany (BGH / BVerfG). Returns structured case records with type classification (civil/criminal/antitrust/bankruptcy/administrative/unknown), status (filed/pending/decided/appealed/unknown), parties extracted from case titles, opinion URLs and verbatim snippets. Cross-case pattern recognition produces severity-ranked signals (P0–P2) for criminal, antitrust, bankruptcy, regulatory, data-breach and IP categories. Use when: due diligence on a counterparty, vendor risk assessment, competitive intelligence (litigation history), regulatory exposure mapping. All sources are public and keyless. Optional env var COURTLISTENER_API_KEY raises US rate limits beyond the default 5 req/s anonymous tier. SLA: ≤25s p95 (all jurisdictions fetched in parallel, 8s budget per source). Quality score: 20 pts per jurisdiction with ≥1 case retrieved, +10 if signals detected, +5–10 if ≥2–3 distinct sources contributed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
date_toNoISO date YYYY-MM-DD — latest filing or decision date to include
date_fromNoISO date YYYY-MM-DD — earliest filing or decision date to include
party_nameYesName of the company or individual to search (e.g. "Apple Inc", "TotalEnergies", "Volkswagen AG")
jurisdictionNoJurisdictions to search. Defaults to all ["US","UK","EU","FR","DE"].

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
casesYes
statusYes
signalsYes
sourcesYes
party_nameYes
quality_scoreYes
by_jurisdictionYes
jurisdictions_searchedYes

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already convey readOnlyHint=true and destructiveHint=false, and the description adds valuable behavioral context: all sources are public and keyless, an optional API key raises rate limits, SLA is ≤25s p95, and a quality scoring mechanism explains result confidence. This goes beyond the annotations and helps the agent understand performance and constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: main action first, then jurisdictions, output details, use cases, API key note, and SLA/quality. Each sentence adds value, though the quality score formula is somewhat detailed and could be trimmed. Front-loaded appropriately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a multi-jurisdiction tool with output schema present, the description covers the tool's scope, use cases, behavioral traits (keyless, SLA, quality score), and parallelism. It does not list limitations (e.g., date coverage per source) but given the breadth, it is sufficiently complete for an agent to select and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes all five parameters with 100% coverage, including the jurisdiction enum and default behavior. The description adds some context about source-specific budget and quality scoring but does not materially enhance understanding of parameter semantics beyond the schema. Baseline 3 applies since schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Aggregate') and clearly defines the resource ('court filings, judgments and litigation records') across five named jurisdictions. It distinguishes this tool from siblings by specifying the exact legal databases and the multi-jurisdictional scope, making its purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists 'Use when:' scenarios (due diligence, vendor risk assessment, competitive intelligence, regulatory exposure mapping), providing clear context for selection. It does not mention when not to use the tool or name alternative tools, so it falls short of the highest bar for explicit exclusions/alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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