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ttpears

GitLab MCP Server

by ttpears

Custom GraphQL Query

execute_custom_query

Run custom GraphQL queries to filter GitLab issues by assignee, author, or labels when search returns zero results. Use pagination to handle large datasets.

Instructions

Execute custom GraphQL queries for complex filtering (e.g., issues with assigneeUsernames: ["user"], labelName: ["bug"]). Use this for structured filtering by assignee/author/labels when search tools return 0 results. Use pagination and limit complexity to avoid timeouts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesGraphQL query string. Example: query { issues(assigneeUsernames: ["cdhanlon"], state: opened, first: 50) { nodes { iid title webUrl } } }
variablesNoVariables for the GraphQL query
requiresWriteNoHint that this needs write access. Mutations are auto-detected and always write-gated regardless of this flag.
userCredentialsNoYour GitLab credentials (optional — falls back to the configured env token if not provided)
Behavior3/5

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

Annotations provide basic hints (readOnlyHint false, etc.), but the description adds some context: 'limit complexity to avoid timeouts' hints at potential performance issues. The parameter 'requiresWrite' in schema clarifies write behavior. However, the description does not elaborate on side effects, error modes, or auth specifics beyond the schema.

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?

Three sentences, each serving a distinct purpose: stating purpose, giving usage guidance, and providing a caution. Front-loaded with the core action. No extraneous words.

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 presence of 4 parameters (1 required), no output schema, and nested objects in credentials, the description covers the main use case and fallback scenario. It includes usage warnings (pagination, timeouts). However, it lacks explanation of return format or error handling, which would be helpful for a tool with no output schema.

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 coverage is 100%, so baseline is 3. The description provides an example query in the input schema and reinforces usage context. However, it does not add substantial new meaning beyond the schema's parameter descriptions. The value added is contextual (when to use the query parameter).

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 clearly states it executes custom GraphQL queries for complex filtering, with an example. It distinguishes itself from search tools by positioning it as a fallback when search returns 0 results. However, it does not explicitly differentiate from all sibling tools; many are distinct read/write operations.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'for structured filtering by assignee/author/labels when search tools return 0 results'. Also provides guidance on usage: 'Use pagination and limit complexity to avoid timeouts.' This gives clear context for selection.

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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