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rse_policy_builder

Read-only

Architecte de politique RSE — Gapup agent-payable C-suite expertise (SUSTAINABILITY). Returns a structured, audited deliverable. Reference case: TechCorp SAS — Politique RSE 2025-2028 (500 FTE, €60M CA, SaaS B2B France). Inputs are validated server-side — send the documented case fields.

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.
focusNo
valuesYes
companyYes
ambitionsYes
targetLabelsNo
currentInitiativesNo
targetStakeholdersYes

TDQS

B3.3/5.0
Behavior4/5

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

Annotations include readOnlyHint=true, which already signals a safe read operation. The description adds that inputs are validated server-side and that the output is an audited deliverable, providing useful behavioral context beyond the annotations. No contradictions are present.

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 relatively concise at three sentences and front-loads the core purpose ('Architecte de politique RSE'). The reference case is informative but includes some marketing fluff ('Gapup agent-payable C-suite expertise') that could be trimmed without losing value.

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

Completeness2/5

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

This is a complex tool with 8 parameters, nested objects, and no output schema. The description only says 'Returns a structured, audited deliverable' without explaining what that deliverable contains, how to use the async option, or how to interpret the result. It also does not clarify the 'documented case fields' despite being the main invocation guidance.

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

Parameters2/5

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

Schema description coverage is only 13% (async parameter only), so the description must compensate, but it does not explain the parameters. The reference case hints at some company fields (500 FTE, €60M revenue, SaaS B2B France) but does not clarify values, ambitions, targetStakeholders, or optional fields. This is a significant gap for a tool with 8 parameters.

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

Purpose4/5

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

The description identifies the tool as an 'Architecte de politique RSE' (RSE policy architect) and states it returns a structured, audited deliverable, which clearly conveys that the tool builds corporate social responsibility policies. It is distinguished from many generic sustainability tools by focusing on policy creation, though it does not explicitly name sibling tools for differentiation.

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

Usage Guidelines3/5

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

The description provides context (C-suite expertise, sustainability domain) and an example case (TechCorp SAS) that implies when to use the tool, but it does not explicitly state when to choose this over other ESG tools like action_plan_esg or sustainability_report. There are no exclusions or alternative references, only a vague sense of high-level policy creation.

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.