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mcp-gitlab-crunchtools

mcp-gitlab-crunchtools

by crunchtools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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List Milestones Tool

list_milestones_tool
Read-only

List and filter milestones for a GitLab project by state or title, with pagination, to track project milestones and release progress.

Instructions

List milestones for a GitLab project.

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": "list_milestones_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageNoPage number (default: 1)
stateNoFilter by state (active, closed, all)active
searchNoFilter by title
per_pageNoResults per page, max 100 (default: 20)
project_idYesProject ID or path

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.1

TDQS

B3.1/5.0
Behavior2/5

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

The readOnlyHint=true annotation already declares the safety profile, so the description's burden is lighter; however, it adds nothing beyond the name — no mention of result ordering, default state filtering (schema default is 'active'), pagination limits, or what a milestone record contains. For a read-only list endpoint the description could have supplied useful behavioral notes and instead supplies none.

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 single sentence is front-loaded with the verb and resource and contains no waste. It is concise to the point of under-specification rather than verbosity, but structurally it is clean.

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

Completeness4/5

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

Given the simple read-only nature of the tool, full schema coverage with descriptions, an output schema, and a readOnlyHint annotation, the description is sufficient for an agent to know what the tool returns in broad terms. Usage guidance is the main missing piece, but the structured data compensates substantially.

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 100%, so all five parameters are already documented with types and defaults (page, per_page, state, search). The description adds no additional meaning about how these parameters interact or filter results, so the baseline of 3 is appropriate.

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 states a specific verb and resource ('List milestones') applied to a scoped entity ('for a GitLab project'), which is immediately understandable. It does not differentiate itself from the sibling mutation tools (create_milestone_tool, update_milestone_tool), but the read-vs-write distinction is largely self-evident from the verb.

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?

There is no guidance on when to use this tool versus alternatives, no prerequisites (e.g. project_id format requirements), and no mention of pagination or default filtering behavior. The agent must infer all usage context from the schema and annotations.

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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