Which integrations are available for this server?

Provides tools to analyze GitHub Actions workflow performance, audit workflow configurations, and retrieve billing usage data from GitHub. Offers MCP tools for GitHub Actions workflow timing analysis, configuration auditing, and billing/cache usage insights.

How do I use gha-intel-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@gha-intel-mcp What's the p95 runtime for our CI workflow runs?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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gha-intel-mcp

by barissozudogru

Overview Schema Source Related Servers Score Discussions Admin

JavaScript

Hybrid

An MCP server for GitHub Actions workflow timing analysis, configuration auditing, and billing insights.

Tools

Tool	Description
`list_workflow_performance`	Computes average, min, max, and p95 duration statistics for recent workflow runs.
`analyze_workflow_config`	Evaluates workflow YAML for caching, parallelism, concurrency, artifacts, checkout depth, timeouts, runner pinning, Docker caching, and triggers.
`get_billing_usage`	Returns Actions billing minutes and estimated cost by runner type, plus per-repo cache utilisation.

Related MCP server: copilot-usage-mcp

Requirements

Node.js >= 18 (uses native fetch)
A GitHub personal access token with repo and read:org scopes

Setup

Three transport modes are available. Choose whichever fits your deployment:

Option A: stdio (local, recommended for desktop clients)

The server runs as a subprocess of the MCP client over stdin/stdout. No network port required.

Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json (macOS) %APPDATA%\Claude\claude_desktop_config.json (Windows)

{
  "mcpServers": {
    "gha-intel": {
      "command": "npx",
      "args": ["-y", "@barissozudogru/gha-intel-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Claude Code

claude mcp add gha-intel -e GITHUB_TOKEN=ghp_your_token -- npx -y @barissozudogru/gha-intel-mcp

Cursor

~/.cursor/mcp.json

{
  "mcpServers": {
    "gha-intel": {
      "command": "npx",
      "args": ["-y", "@barissozudogru/gha-intel-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Windsurf

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "gha-intel": {
      "command": "npx",
      "args": ["-y", "@barissozudogru/gha-intel-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

VS Code + Copilot

.vscode/mcp.json (workspace) or user settings

{
  "servers": {
    "gha-intel": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@barissozudogru/gha-intel-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Cline

Open Cline settings, navigate to MCP Servers, and add:

{
  "mcpServers": {
    "gha-intel": {
      "command": "npx",
      "args": ["-y", "@barissozudogru/gha-intel-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Continue.dev

~/.continue/config.yaml

mcpServers:
  - name: gha-intel
    command: npx
    args:
      - -y
      - "@barissozudogru/gha-intel-mcp"
    env:
      GITHUB_TOKEN: ghp_your_token

Zed

~/.config/zed/settings.json

{
  "context_servers": {
    "gha-intel": {
      "command": {
        "path": "npx",
        "args": ["-y", "@barissozudogru/gha-intel-mcp"],
        "env": {
          "GITHUB_TOKEN": "ghp_your_token"
        }
      }
    }
  }
}

JetBrains (IntelliJ, PyCharm, WebStorm, etc.)

Go to Settings > Tools > AI Assistant > MCP and add:

{
  "mcpServers": {
    "gha-intel": {
      "command": "npx",
      "args": ["-y", "@barissozudogru/gha-intel-mcp"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Option B: HTTP (remote or cloud clients)

Start the server in HTTP mode and point clients at the endpoint:

GITHUB_TOKEN=ghp_your_token npx @barissozudogru/gha-intel-mcp --http
# Server listens on http://0.0.0.0:3000/mcp
# Health check: http://localhost:3000/health

Or set via environment variable instead of the flag:

TRANSPORT=http PORT=3000 GITHUB_TOKEN=ghp_your_token npx @barissozudogru/gha-intel-mcp

Cursor (HTTP)

~/.cursor/mcp.json

{
  "mcpServers": {
    "gha-intel": {
      "url": "http://localhost:3000/mcp"
    }
  }
}

VS Code + Copilot (HTTP)

.vscode/mcp.json

{
  "servers": {
    "gha-intel": {
      "type": "http",
      "url": "http://localhost:3000/mcp"
    }
  }
}

Windsurf (HTTP)

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "gha-intel": {
      "serverUrl": "http://localhost:3000/mcp"
    }
  }
}

Continue.dev (HTTP)

~/.continue/config.yaml

mcpServers:
  - name: gha-intel
    url: http://localhost:3000/mcp

Option C: Docker

docker build -t gha-intel-mcp .
docker run -p 3000:3000 -e GITHUB_TOKEN=ghp_your_token gha-intel-mcp

The container starts in HTTP mode by default. Point your client at http://localhost:3000/mcp.

Tool Reference

list_workflow_performance

Fetch real run timing data and compute job-level statistics.

Parameter	Type	Required	Description
`owner`	string	yes	GitHub owner (user or org)
`repo`	string	yes	Repository name
`workflow_id`	string	yes	Workflow file name (e.g. `ci.yml`) or numeric ID
`count`	number	no	Number of recent runs to analyse (default: 10, max: 100)

Output: Per-job and per-step timing stats (avg, min, max, p95), overall run timing, and a list of recent run conclusions.

analyze_workflow_config

Parse and audit a workflow YAML for optimisation opportunities.

Parameter	Type	Required	Description
`workflow_content`	string	yes	Full YAML content of the workflow file

Output: Findings grouped by severity (critical / warning / info / good) across nine categories, each with a concrete recommendation.

Categories analysed: Dependency caching, matrix strategy and fail-fast, concurrency groups and cancel-in-progress, artifact uploads, git checkout depth, job timeout-minutes, runner version pinning, Docker layer caching, and trigger path filters.

get_billing_usage

Retrieve billing and cache consumption data.

Parameter	Type	Required	Description
`owner`	string	yes	GitHub username or organisation
`repo`	string	no	Repository name for repo-scoped cache and run stats

Output: Total minutes used, plan utilisation, estimated cost broken down by runner type (Ubuntu / macOS / Windows / large runners), plus per-repo cache size and utilisation percentage.

Environment Variables

Variable	Required	Description
`GITHUB_TOKEN`	yes	GitHub personal access token. Requires `repo` scope for private repos, `read:org` for org billing.
`TRANSPORT`	no	Set to `http` to enable HTTP mode (default: stdio).
`PORT`	no	HTTP port when running in HTTP mode (default: `3000`).

License

MIT

Available Tools

3 tools

analyze_workflow_configAnalyze Workflow ConfigA

Parse a GitHub Actions workflow YAML (provided as a string) and identify optimization opportunities: missing caches, matrix strategy, concurrency controls, slow dependency installs, artifact handling, and more.

ParametersJSON Schema

Name	Required	Description	Default
`workflow_content`	Yes	Full YAML content of the GitHub Actions workflow file

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden of disclosure. It says the tool 'parses' and 'identifies' optimization opportunities, implying read-only analysis. However, it doesn't state whether it modifies anything, whether it requires valid YAML, or what happens on invalid input. The description doesn't contradict annotations, but it adds minimal behavioral detail beyond the action itself.

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 a single sentence that packs substantial information: the input format (YAML string), the action (parse and identify optimization opportunities), and specific examples of what it looks for. Every element earns its place, and it's front-loaded with the core purpose.

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 it's a single-parameter tool with a descriptive schema, no output schema, and no annotations, the description covers the essential aspects: input format, purpose, and analysis areas. It's quite complete for its simplicity. The only gap is specifying expected output format, but since no output schema exists, the description should ideally mention what the analysis returns, though it's not critical for invocation.

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 single parameter 'workflow_content' has a clear schema description 'Full YAML content of the GitHub Actions workflow file', and schema coverage is 100%. The description adds the context of what the tool does with it (parse and analyze) but doesn't add format expectations beyond what the schema says. Baseline 3 is appropriate.

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 parses a GitHub Actions workflow YAML string and identifies optimization opportunities, listing specific areas (caches, matrix strategy, concurrency, dependency installs, artifacts). This is a specific verb 'analyze' with a clear resource 'workflow config' and distinct purpose from siblings like list_workflow_performance and get_billing_usage, which focus on performance listing and billing.

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 when to use: when you have a workflow YAML string to analyze for optimization. It does not explicitly contrast with siblings or mention when not to use. While the purpose is clear, there is no explicit guidance on alternatives or exclusions, so it's adequate but lacks depth.

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

get_billing_usageGet Billing UsageA

Retrieve GitHub Actions billing and cache usage statistics for an owner (user or org), optionally scoped to a specific repository.

ParametersJSON Schema

Name	Required	Description	Default
`repo`	No	Optional repository name to scope usage. When provided, returns repo-level cache stats and recent run timing.
`owner`	Yes	GitHub username or organisation name

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description must carry full weight. It discloses that it retrieves statistics, but does not clarify whether this is a read-only operation (likely safe), what data is returned (cache stats, recent run timing), or any potential side effects. It doesn't mention rate limits, authorization needs, or whether it only works for paid plans. This is a basic functional description with no behavioral depth.

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 description is a single, well-structured sentence that is front-loaded with the verb and resource. It is concise and avoids redundancy, but it could potentially be split into two sentences for clarity (e.g., separating the scoping condition). Still, it is efficient with no fluff.

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

Completeness3/5

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

The tool is relatively simple with only 2 params and no output schema. The description covers the main purpose and scoping, which is adequate for a read-only retrieval tool. However, given no annotations and no output schema, it would benefit from mentioning whether the returned data is summarized or detailed, or noting that repo-scoped usage may not include owner-level billing aggregates. Completeness is acceptable but leaves gaps.

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?

Schema description coverage is 100%, with both parameters having descriptions. The description adds that 'repo' returns 'repo-level cache stats and recent run timing,' which supplements the schema by specifying the effect of providing the optional repo. However, it doesn't detail the exact format of 'owner' (e.g., case sensitivity) or the structure of returned data, but it adds meaningful scoping context beyond the schema.

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 verb 'Retrieve' and the specific resource 'GitHub Actions billing and cache usage statistics', and it distinguishes scope options (owner vs. repo). It differentiates from siblings like 'list_workflow_performance' and 'analyze_workflow_config' by targeting billing/cache, while siblings focus on performance/config.

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 usage by specifying optional scoping to a repository, but it does not explicitly state when to use this vs. siblings, nor provide exclusions. There is no mention of prerequisites (e.g., required permissions) or when owner-only vs. repo-scoped is appropriate. Context like billing queries is implied but not elaborated.

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

list_workflow_performanceList Workflow PerformanceA

Fetch the last N workflow runs and compute job-level timing statistics (avg, min, max, p95) across those runs. Useful for identifying slow jobs and trends.

ParametersJSON Schema

Name	Required	Description
`repo`	Yes	GitHub repository name
`count`	No	Number of recent runs to analyse (default: 10, max: 100)
`owner`	Yes	GitHub repository owner (user or org)
`workflow_id`	Yes	Workflow file name (e.g. ci.yml) or numeric workflow ID

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that it fetches runs and computes statistics, but does not mention API rate limits, authentication needs, or what happens with insufficient data. The description adds some behavioral context (computing stats) but omits other factors like data retention or pagination.

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 two sentences: the first states the function, the second states the use case. No waste, fully front-loaded, concise.

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 it's a computation tool with no output schema and no annotations, the description effectively conveys purpose and usage. Missing details like output format or edge cases (e.g., no runs found) are not critical given the tool's simplicity. It covers essential aspects for an agent.

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 the schema fully documents all four parameters. The description adds no additional parameter-level details beyond what the schema provides, so baseline 3 is appropriate.

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 fetches the last N workflow runs and computes job-level timing statistics, which is a specific verb+resource combination. It also distinguishes itself from siblings like analyze_workflow_config and get_billing_usage by focusing on performance statistics derived from runs.

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

Usage Guidelines4/5

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

The description implies usage for performance analysis ('Useful for identifying slow jobs and trends') but does not explicitly say when not to use it or mention alternatives. It provides clear context for when this tool is appropriate, but lacks explicit exclusions.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

3 tool updatesv0.4.3Aug 20, 2026
- First observedanalyze_workflow_config
- First observedget_billing_usage
- First observedlist_workflow_performance

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.9/5.0

Scored 2026-08-20 15:26 across 3 tools

Disambiguation5/5

Each tool addresses a clearly distinct aspect: runtime performance metrics, static configuration analysis, and billing/cost usage. There is no overlap in purpose or data source, so an agent can reliably select the right tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: list_workflow_performance, analyze_workflow_config, get_billing_usage. The verbs (list, analyze, get) and nouns clearly indicate the action and subject, maintaining uniformity across the set.

Tool Count4/5

With only 3 tools, the server is minimally scoped but each tool serves a distinct, valuable function within the GitHub Actions intel domain. The count is within the typical 3-15 range, though one could argue it's slightly on the lower end for comprehensive coverage.

Completeness3/5

The tools cover performance metrics, config analysis, and billing, but there's a notable gap: no tool to fetch the workflow YAML directly, forcing an external step for configuration analysis. Additionally, lifecycle operations like listing workflows or runs are missing, though performance stats partially address that.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

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