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mcp-knowledge

by io.github.getgapup

corporate_registry_lookup

Read-onlyIdempotent

Resolve legal information about a company from its national corporate registry. Returns a normalised, sourced company profile: legal status, registration number, directors, shareholders, recent filings, registered address, share capital, and a quality score (0–100). Coverage: France (INPI, keyless — full SIREN/SIRET with directors), 3M+ entities worldwide via GLEIF LEI (keyless, large companies), UK (Companies House, optional key), Netherlands (KvK, optional key), and OpenCorporates (token required since 2026). Sources are tried in cascade; quality_score increases with each source that succeeds. When to use: due-diligence, KYC screening, supplier verification, M&A research, or any workflow needing verified company identity and legal status. Optional env vars: COMPANIES_HOUSE_API_KEY (UK), KVK_API_KEY (NL), OPENCORPORATES_API_TOKEN (OpenCorporates token).

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.
countryNoISO 3166-1 alpha-2 country code (e.g. 'FR', 'GB', 'NL', 'DE', 'SG', 'AU', 'US'). If omitted, inferred from legal suffix in company name, then falls back to global search.
identifierNoOptional registry identifier for a fast direct lookup: SIREN (FR, 9 digits), Companies House number (GB, 8 chars), KvK number (NL, 8 digits), etc.
company_nameYesCompany name or trading name to look up (e.g. 'Sanofi', 'Tesco PLC', 'Notion Labs Inc')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
registryYes
directorsYes
freshnessYesISO timestamp
identifierYes
legal_formNo
legal_nameNo
company_nameYes
jurisdictionYes
shareholdersYes
quality_scoreYes0-100 confidence score
share_capitalNo
filings_recentYes
incorporation_dateNo
registered_addressNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare read-only, idempotent, and non-destructive. The description adds valuable behavioral details: cascade source fallback, quality_score computation, and optional auth keys. No contradictions.

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 moderately long but well-organized: purpose, output, coverage, cascade mechanism, use cases, env vars. Each sentence adds value, though minor trimming could improve conciseness.

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

Completeness5/5

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

Given the presence of an output schema and rich annotations, the description covers all essential aspects: input semantics, behavior, coverage, use cases, and optional auth. It is fully self-contained for an AI agent.

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

Parameters4/5

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

Schema covers all parameters with descriptions (100% coverage). The description enhances this with concrete examples (SIREN, Companies House number, KvK) and explains fallback logic for country inference, adding meaning beyond schema.

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 clearly states the tool resolves legal information from national corporate registries and lists specific output fields. It distinguishes itself from siblings like kyc_screener by focusing on registry data.

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?

Explicitly states when to use (due-diligence, KYC, supplier verification, M&A) and lists optional API keys. Does not provide direct comparisons to alternatives but the use cases are clear.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources