Academic Writer MCP

Lister les exigences

extract_requirements

Read-onlyIdempotent

Retrieve and filter a project’s requirements by type and status, including extracted and user-added items, to organize tasks, objectives, and constraints.

Instructions

Liste les exigences du projet (extraites ou ajoutées par l'utilisateur), filtrables par type et statut.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>"
    },
    "name": "extract_requirements"
  }
}

Input Schema

Name	Required	Description	Default
`kind`	No
`status`	No
`project_id`	Yes

Output Schema

Name	Required	Default
`count`	Yes
`notice`	No	Exigences extraites automatiquement : à confirmer ou rejeter par l'utilisateur (manage_requirement). Les consignes originales font foi.
`by_kind`	Yes
`project_id`	Yes
`requirements`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Nov 9, 2026

TDQS

B3.3/5.0

Behavior3/5

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

The annotations already declare the operation safe, idempotent, and closed-world, so the safety profile is covered. The description adds that the list mixes machine-extracted and user-added requirements, which is genuinely useful behavioral context, but says nothing about ordering, pagination, or what an empty result means. That is a moderate addition over the structured data, not a rich one.

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?

A single front-loaded sentence with the resource, its scope, its provenance, and its filters; no filler and nothing buried mid-clause.

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

Completeness3/5

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

An output schema exists, so return values need not be described, and the annotations cover safety, but for a three-parameter listing tool with zero schema documentation the definition should still say what the filters default to and how the listing relates to the sibling extract/analyze tools. It is adequate rather than 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 description coverage is 0%, so the description must compensate, and it partially does by mapping its 'type'/'statut' wording onto the actual filters and confirming both are optional. It never explains the enum values for 'kind' (task, step, norm, deadline, etc.), nor what a null filter returns, leaving the bulk of parameter meaning to the title-only schema fields.

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 gives a specific verb and resource ('Liste les exigences du projet') and clarifies the two origins of the data (extracted or user-added), which usefully counteracts the misleading tool name 'extract_requirements'. It does not name or distinguish itself from any sibling tool, so it stops short of a 5.

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

Usage Guidelines2/5

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

The only operative guidance is that results can be filtered by type and status; there is no statement of when to call this tool, when not to, or what other tool to use for a different view of requirements. An agent must infer usage entirely from the purpose sentence.

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

Deploy Server