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mcp-knowledge

by io.github.getgapup

ai_governance_full_report_result

Read-onlyIdempotent

Poll the result of an ai_governance_full_report_async job. Returns status=pending while running, status=completed with the full EU AI Act governance audit report once done (risk_tier, compliance checklist Articles 9-15/50/53-55, Annex IV documentation gaps, ISO 42001 alignment, deadlines 2025-2029, cost estimate, top-10 recommendations P0/P1/P2, compliance_score), status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h). Call this after the eta_seconds hint returned by ai_governance_full_report_async (~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job_id returned by ai_governance_full_report_async (prefix: aigfr_)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which are consistent. The description adds behavioral details: returns status=pending while running, status=completion with full report, status=failed on error, status=not_found for unknown/expired job_id, TTL 24h. No contradiction.

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

Conciseness5/5

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

Two sentences with high information density. Front-loaded with purpose. Every part adds value: purpose, status outcomes, timing hint, and parameter context. No wasted words.

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?

For an async polling tool, the description covers all necessary aspects: what the tool does, when to call, possible return statuses, error conditions, TTL, and outputs (report contents). Even though an output schema exists, the description enumerates key fields. Highly complete.

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?

Schema coverage is 100% with a single required parameter job_id described as 'The job_id returned by ai_governance_full_report_async (prefix: aigfr_)'. The description doesn't add new parameter semantics beyond the schema, but given high coverage, baseline 3 is appropriate.

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 its purpose: 'Poll the result of an ai_governance_full_report_async job.' It distinguishes itself from siblings by being the polling counterpart to the async variant. The verb 'poll' and the specific resource are unambiguous.

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

Usage Guidelines5/5

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

Explicitly instructs to call this after the eta_seconds hint from ai_governance_full_report_async (~90s). Describes the various status returns (pending, completed, failed, not_found) and the TTL. This provides clear when-to-use guidance.

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.

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