mister-parc-mcp

Librairie

parc_librairie

Read-onlyIdempotent

Retrieve a library's versions, which repositories use each version, the latest upstream release, and the lag behind upstream.

Instructions

Une librairie du parc : ses versions, quels dépôts sont sur laquelle, la dernière version publiée en amont et le retard.

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": {
      "paquet": "<paquet>"
    },
    "name": "parc_librairie"
  }
}

Input Schema

Name	Required	Description	Default
`paquet`	Yes	Nom exact du paquet, par exemple lucide-react ou Node.js.

Output Schema

Name	Required	Description	Default
`genere`	Yes	Date du relevé qui fonde la réponse (ISO 8601).
`librairie`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 31, 2026

TDQS

C2.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety profile is covered. The description adds meaningful context beyond them: it explains that upstream publication data is consulted (justifying openWorldHint) and that a version lag is computed, though it says nothing about freshness or upstream lookup failures.

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?

A single compact sentence that front-loads the entity and then lists the return facets. Slightly awkward phrasing ('quels dépôts sont sur laquelle') but no wasted content.

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 spelled out, and the read-only nature is annotated. What is missing is scope clarification: whether this is estate-wide or per-package detail, and how it relates to the lagging-libraries sibling that shares nearly the same name space.

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?

One parameter with 100% schema description coverage, including concrete examples (lucide-react, Node.js). The description implies the tool is scoped to a single package but adds no syntax or matching rules ('nom exact' is already in the schema), so the baseline 3 applies.

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

Purpose3/5

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

The description states the resource (a library in the estate) and enumerates the facets returned: versions, which repos are on which version, latest upstream release, and lag. But there is no verb ('get', 'look up') and no explicit differentiation from close siblings like parc_librairies_en_retard or parc_depots, so the agent must infer this is the per-package detail view.

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?

No statement of when to use this tool versus alternatives. The contrast with parc_librairies_en_retard (the aggregate lagging-libraries list) is strongly implied by the shared vocabulary but never named, so the routing decision is left to inference.

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