gapup-mcp

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

Name	Required	Description	Default
`job_id`	Yes	The job_id returned by ai_governance_full_report_async (prefix: aigfr_)

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior5/5

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

Description discloses all statuses (pending, completed, failed, not_found), TTL 24h, and details of the report content. Annotations already indicate read-only and idempotent; description adds significant behavioral context beyond annotations.

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?

Single paragraph containing all necessary information without redundancy. Could be slightly more structured but is efficient and front-loaded.

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 complexity of polling an async job with multiple statuses and a detailed report, the description covers all aspects: statuses, TTL, usage timing, and report contents. Output schema exists, so return values are documented.

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?

Only one parameter (job_id) with schema description covering prefix constraint. Schema coverage is 100%, so description adds minimal extra value 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?

Description clearly states verb 'Poll' and resource 'result of an ai_governance_full_report_async job'. Distinguishes from sibling tools by specifying it's for polling results after async initiation.

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 call: after the eta_seconds hint (~90s). Does not explicitly mention when not to use, but context implies it should only be called after the async job.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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