GitHub MCP Server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a distinct aspect of GitHub PR workflows: authentication, listing, comments, checks, and detailed info. There is no functional overlap, so an agent can easily select the appropriate tool based on the task.
Naming Consistency5/5All tools follow a consistent github_verb_noun pattern (set_token, list_pull_requests, get_pr_details, etc.). The naming is uniform and predictable, making the tool set easy to navigate.
Tool Count5/5With five tools, the server is well-scoped around pull request operations. Each tool serves a clear purpose without redundancy, falling comfortably within the ideal range for a focused server.
Completeness2/5The tool set is read-only for pull requests, lacking create, update, or delete operations. Despite the server name suggesting broad GitHub coverage, no issues or repository management are included, leaving significant gaps for typical workflows.
Average 3.7/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it only says what is listed. It does not disclose pagination, response structure, rate limits, or the performance cost of including comments (the 'may slow down' caveat appears in schema, not here).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence conveys the core function and optional features with no wasted words. Ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a list operation: it names the resource, scope, and options. However, it lacks details on pagination, sorting, and does not mention when to prefer sibling tools, leaving some gaps for an agent unfamiliar with the GitHub API.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all 5 parameters with descriptions (100% coverage). The description's mention of 'optionally comments and pipeline status' maps to include_comments and include_checks, but adds no new meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all pull requests in a repository, with details like creator, labels, and optional comments/checks. This explicitly distinguishes it from siblings like github_get_pr_details (single PR) and github_get_pr_comments/checks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. Sibling tools exist for comments and checks, but the description does not mention them or clarify when to use the dedicated tools instead of this list endpoint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states the action and purpose, but does not disclose whether the token is persisted, whether it overwrites an existing token, or any security implications. This lack of side-effect information is a significant gap for a mutation-like tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short, front-loaded sentence with no filler. It communicates the essential action and purpose in the fewest possible words, earning every character.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with a single parameter and no output schema, so the description is minimally adequate. However, it lacks context about how this tool relates to sibling GitHub tools (e.g., whether setting the token is required before using them) and does not explain persistence or scope, leaving clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'token' described as 'GitHub personal access token'. The description adds no additional meaning beyond what the schema already provides, which aligns with the baseline score for full coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Set' and names the resource 'GitHub personal access token', clearly distinguishing this from sibling tools that list/get PR data. It unambiguously states the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for authentication' implies it is a prerequisite/configuration step, but it does not explicitly state when to use it relative to sibling tools or mention any exclusion conditions. There is no indication that it should be used before other GitHub API calls, leaving usage context only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only says 'Get', implying read-only, but does not mention any prerequisites, permissions, rate limits, or what happens when a PR has no checks. The result format is also not described beyond 'check runs and status'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, concise sentence that starts with the verb 'Get' and immediately states the resource and scope. No filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with 3 parameters and no output schema. The description communicates the core function well enough for an agent to understand what is returned (CI/CD check runs and status). It lacks some detail about edge cases, but for a basic read operation this is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all three parameters with descriptions (owner, repo, pull_number). The description adds no extra parameter-specific meaning, only restating the target pull request context. Schema coverage is 100%, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Get') and resource ('CI/CD check runs and status') targeted at a specific pull request. This distinguishes it from sibling tools like github_get_pr_comments and github_get_pr_details, which focus on different aspects of a PR.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use this tool: when you need CI/CD check runs and status for a specific pull request. It does not explicitly mention alternatives or exclusions, but the purpose is specific enough to guide selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description usefully discloses that both issue comments and review comments are included, which adds context beyond the tool name. However, with no annotations, it does not mention pagination, authentication prerequisites, or error behavior. The description carries the full burden but only partially covers behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action and scope. Every word adds value, and there is no filler or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple three-parameter getter, the description adequately states what the tool does, but it lacks details about the return format, pagination, and authentication. Since there is no output schema and no annotations, this leaves some gaps for an agent deciding how to invoke and interpret the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers all three parameters with generic descriptions (e.g., 'Repository name'). The tool description adds no extra parameter-specific context, so it earns the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', the resource 'comments', and the scope 'for a specific pull request', distinguishing it from sibling tools like github_get_pr_details or github_get_pr_checks. Explicitly mentions both issue comments and review comments, leaving no ambiguity about what is returned.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes it clear when to use this tool: when you need all comments for a pull request. It does not explicitly list alternatives or exclusions, but the context is obvious given the sibling tools. No conflicting guidance exists, so it earns a 4 for clear context without exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. The verb 'Get' implies a read-only operation, and the schema parameter description warns that the full diff may be large, but the tool description itself does not explicitly state that it does not modify the PR, does not mention authentication requirements (though the sibling github_set_token hints at this), or mention rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action and purpose and lists the key components without any filler. It is efficiently sized for the information it conveys.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description must indicate what the tool returns; it lists the main components (files changed, commits, optional diff) which is sufficient for a read operation. It does not describe the exact response structure or additional PR metadata, but this is a reasonable level of completeness for a git PR details tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides complete descriptions for all four parameters (100% coverage), including the include_diff flag and its potential size. The description adds minimal value beyond this, as it only mentions 'optionally the full diff' without further syntax or usage details, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed information about a specific pull request'), and explicitly lists the included content (files changed, commits, optionally the full diff). This distinguishes it from sibling tools like github_list_pull_requests (which lists PRs) and github_get_pr_comments (which gets comments).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates the tool is for retrieving details of a single, specific pull request, contrasting with the sibling tool github_list_pull_requests which is for listing multiple PRs. However, it does not explicitly name these alternatives or state when not to use it (e.g., for comments or checks).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
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