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Glama

diagnostic_pilotage

Returns effective server settings—scanned roots, exclusions, scan depth, ripgrep presence, cache duration, and security guarantees—to diagnose missing projects, empty searches, or slow scans.

Instructions

Renvoie la configuration effective du serveur Pilotage : racines scannées, dossiers exclus, profondeur de scan, présence de ripgrep, durée du cache, et rappel des garanties de sécurité. Appelle cet outil quand un résultat te surprend (un projet manquant, une recherche vide, une lenteur) pour comprendre ce que le serveur voit réellement du disque avant de conclure.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "diagnostic_pilotage"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the returned configuration dimensions and implies a passive read ("Renvoie"), and the mention of security guarantees and cache duration adds real behavioral context about how server results may diverge from disk reality. It stops short of stating explicitly that the call has no side effects, nor how expensive or how large the response is.

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: the first front-loads what is returned, the second front-loads when to call it. Every clause earns its place by naming a concrete configuration field or a concrete failure symptom, with no filler.

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?

There is no output schema, so enumerating the returned configuration fields in the description is exactly the right compensation. For a zero-parameter diagnostic tool, purpose, contents, and invocation context are all covered, leaving nothing an agent would need before calling it.

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

Parameters4/5

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

The tool takes zero parameters, so there is nothing for the description to disambiguate and the schema imposes no burden. Baseline of 4 applies; no parameter-level value could be added.

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?

"Renvoie la configuration effective du serveur Pilotage" is a specific verb+resource, and the enumeration (racines scannées, dossiers exclus, profondeur, ripgrep, cache, garanties de sécurité) makes the content unambiguous. It is clearly a self-diagnostic tool, distinct from siblings like lister_projets or chercher_partout which query actual data.

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?

Gives concrete triggering conditions: a missing project, an empty search, a slowdown, and the instruction to understand what the server sees "avant de conclure". That is strong conditional guidance, but it names no competing alternative or explicit when-not case (e.g. do not use for normal queries).

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