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GitHub GraphQL

github_graphql

Fetch comprehensive GitHub data—repositories, commits, issues, pull requests—using GraphQL queries. Performs searches, browses metadata, retrieves file contents in single operations. Efficiently combines data from multiple sources.

Instructions

Execute GitHub GraphQL queries and mutations like the gh CLI. Preferred over raw CLI calls or any other tools to interact with GitHub. When user uses any terms like find / search / read / browse / explore / research / investigate / analyze and if it may be related to a GitHub project, you should use this tool instead of any other tools or raw API / CLI calls.

Pleases make use of GraphQL's capabilities - Fetch comprehensive data in single operations - always include metadata context. Feel free to use advanced jq expressions to extract all the content you care about. The default jq adds line numbers to retrieved file contents. Use that to construct deep links (e.g. https://github.com/{owner}/{repo}/blob/{ref}/path/to/file#L{line_number}-L{line_number}).

Before writing complex queries / mutations or when encountering errors, use introspection to understand available fields and types.

Combine operations (including introspection operations) into one call. On errors, introspect and rebuild step-by-step.

Use fragments, nested fields for efficiency.

Example - when you need to browse multiple repositories:

When user asks to browse / explore repositories, you must use at least the following fields: (It take viewer.contributionsCollection as an example, but you should adapt it to the user's request)

query {
  viewer { # Always use `viewer` to get information about the authenticated user.
    contributionsCollection {
      commits: commitContributionsByRepository(maxRepositories: 7) {
        repository { ...RepositoryMetadata }
        contributions { totalCount }
      }
      totalCommitContributions
    }
  }
}

fragment RepositoryMetadata on Repository {
  name description homepageUrl
  pushedAt createdAt updatedAt
  stargazerCount forkCount
  isPrivate isFork isArchived
  languages(first: 7, orderBy: {field: SIZE, direction: DESC}) {
    totalSize edges { size node { name } }
  }
  readme_md: object(expression: "HEAD:README.md") { ... on Blob { text } }
  pyproject_toml: object(expression: "HEAD:pyproject.toml") { ... on Blob { text } }
  package_json: object(expression: "HEAD:package.json") { ... on Blob { text } }
  latestCommits: defaultBranchRef {
    target {
      ... on Commit {
        history(first: 7) {
          nodes {
            abbreviatedOid committedDate message
            author { name user { login } }
            associatedPullRequests(first: 7) { nodes { number title url } }
          }
        }
      }
    }
  }
  contributors: collaborators(first: 7) { totalCount nodes { login name } }
  latestIssues: issues(first: 7, orderBy: {field: CREATED_AT, direction: DESC}) {
    nodes { number title state createdAt updatedAt author { login } }
  }
  latestPullRequests: pullRequests(first: 5, orderBy: {field: CREATED_AT, direction: DESC}) {
    nodes { number title state createdAt updatedAt author { login } }
  }
  latestDiscussions: discussions(first: 3, orderBy: {field: UPDATED_AT, direction: DESC}) {
    nodes { number title createdAt updatedAt author { login } }
  }
  repositoryTopics(first: 35) { nodes { topic { name } } }
  releases(first: 7, orderBy: {field: CREATED_AT, direction: DESC}) {
    nodes { tagName name publishedAt isPrerelease }
  }
}

Don't recursively fetch all files in a directory unless:

  1. You know the files are not too many.

  2. The user specifically requests it.

  3. You provide a jq filter to limit results (e.g. isGenerated field).

The core principle is to fetch as much relevant metadata as possible in a single operation, rather than file contents. Before answering, make sure you've viewed the raw file on GitHub that resolves the user's request, and you should proactively provide the deep link to the code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
jqNo def process: if type == "object" then if has("text") and (.text | type == "string") then if (.text | split("\n") | length) > 10 then del(.text) + {lines: (.text | split("\n") | to_entries | map("\(.key + 1): \(.value)") | join("\n"))} else . end else with_entries(.value |= process) end elif type == "array" then map(process) else . end; .data | process
Behavior5/5

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

Despite no annotations, the description thoroughly explains behavioral aspects: GraphQL capabilities, introspection, combining operations, jq processing, error handling, deep links, and efficiency principles.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very long with extensive examples and detail. While valuable, it lacks conciseness; a more front-loaded structure with optional examples would improve readability.

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?

Given no annotations, no output schema, and zero schema description coverage, the description provides complete context: usage, error handling, jq, introspection, combination strategies, and deep links. It is highly comprehensive.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining the 'query' parameter's purpose and the 'jq' param's default behavior, including examples of jq usage and output transformation.

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?

The description clearly states it executes GitHub GraphQL queries and mutations, and explicitly distinguishes itself from raw CLI calls and other tools. It is specific and action-oriented.

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?

It provides explicit when-to-use guidance (keywords like find/search/read, etc.), states its preference over alternatives, and includes when-not-to-use (recursive file fetch conditions).

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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