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list_github_api_calls_in_run

List all HTTPS calls to GitHub and api.github.com made by jobs in a specific workflow run. Audit API endpoints accessed, detect writes to unexpected repos, and identify surprising API calls from third-party actions.

Instructions

List every HTTPS call to github.com or api.github.com made by jobs in a specific workflow run. Useful for auditing which GitHub API endpoints a workflow touches — detecting writes to unexpected repos, surprising org access, or API calls from third-party Actions. Fast: one upstream API call. Requires a run_id — if the user doesn't supply one, call list_recent_workflow_runs first to find it. Returns calls grouped by job with step/tool attribution. The response has a top-level dashboard_url for the run AND a per-job dashboard_url deep-linking to the network-events tab scoped to that job. When presenting results, include the per-job link next to each job header.

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": {
      "owner": "<owner>",
      "repo": "<repo>",
      "runId": "<runId>"
    },
    "name": "list_github_api_calls_in_run"
  }
}

Input Schema

Name	Required	Description
`owner`	Yes	GitHub organization
`repo`	Yes	Repository name (without owner)
`runId`	Yes	Workflow run ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Apr 24, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Since no annotations provided, the description fully discloses behavior: fast (one upstream call), returns calls grouped by job with step/tool attribution, includes top-level and per-job dashboard_url links. No contradictions.

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?

The description is concise (2-3 sentences) and well-structured, with clear sections for purpose, usage, and response details. No unnecessary words.

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?

Despite no output schema, the description explains the response structure (grouped by job, dashboard links) and provides actionable tips (include per-job link). Given the complexity, this is fully complete for an agent to use correctly.

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?

Input schema has 100% coverage with descriptions for all three parameters. The description adds value by explaining the importance of run_id and suggesting a fallback workflow, but schema already provides the core semantics.

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 the tool lists HTTPS calls to github.com or api.github.com from a specific workflow run, which distinguishes it from sibling tools that handle anomaly detection or general network calls.

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?

Provides explicit usage scenarios: auditing API endpoint usage, detecting unexpected writes. Also gives a prerequisite: if missing run_id, call list_recent_workflow_runs first. This guides the agent on when and how to use the tool.

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