MCP PR Workflow Server

analyze_ci_results

Analyze GitHub Actions workflow runs for a pull request to get a formatted summary of CI results and identify failures.

Instructions

Analyzes GitHub Actions workflow runs for a PR and provides a formatted summary.

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>",
      "pull_number": "<pull_number>"
    },
    "name": "analyze_ci_results"
  }
}

Input Schema

Name	Required	Description	Default
`repo`	Yes
`owner`	Yes
`pull_number`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 10, 2026

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden. It states that the tool analyzes workflow runs, which suggests a read-only operation, but it does not disclose specifics such as what data is accessed, how the summary is generated, potential side effects, authentication requirements, or failure behaviors. There is limited insight beyond the basic function.

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?

One concise sentence communicates the core purpose and output, with no wasted words. However, it may be too sparse, lacking details that would be useful, but for what it is, it is efficiently written.

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

Completeness2/5

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

With no annotations, no output schema, and only a terse description, the tool is underdocumented. The description gives only a high-level view and omits details about the summary structure, edge cases, required permissions, or how the results are obtained, leaving significant gaps for an agent to use the tool correctly.

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?

The input schema has no per-parameter descriptions (0% coverage), so the description must compensate. The description mentions 'PR' and 'GitHub Actions', which implicitly clarifies that pull_number refers to the pull request and owner/repo identify the repository, but it doesn't explicitly describe the parameters or their expected formats. The parameter names are fairly self-evident in this GitHub context.

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 identifies the tool's function: analyzing GitHub Actions workflow runs for a pull request and generating a formatted summary. This distinguishes it from sibling tools like analyze_file_changes or update_pr_status, which focus on different resources or actions.

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

Usage Guidelines3/5

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

The description implies the tool is used when a formatted summary of CI results for a PR is needed, but it provides no explicit guidance on when to use this tool over alternatives, nor does it mention any exclusions or prerequisites. This is minimal inferred usage context.

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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