GitHub Repository Health MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a distinct aspect of repository health: metadata, issues, commits, and workflow runs. There is no overlap or ambiguity between them.
Naming Consistency5/5All four tools follow a consistent get_<resource>_<detail> pattern with snake_case naming. The naming is predictable and easy to extend.
Tool Count5/5Four tools is well-scoped for a read-only repository health MCP. Each tool covers a meaningful health signal without unnecessary redundancy.
Completeness4/5The core health indicators—repo stats, issues, commits, and CI status—are covered. Pull request activity and contributor trends are notable omissions, but the current surface is sufficient for basic health assessments.
Average 3.4/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that data is 'current' but does not mention authentication requirements, rate limits, read-only nature, or any side effects. The word 'Get' weakly implies a read operation, but this is not made explicit.
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, efficient sentence that immediately states the action and the resource. There is no redundant phrasing or filler, and the core information is front-loaded.
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, there is an output schema available, and sibling names provide context. However, the description lacks usage distinctions and behavioral details such as authentication or scope, leaving the agent to infer important context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the two parameters. It does not mention owner or repo at all. While 'owner' and 'repo' are somewhat self-explanatory in a GitHub context, the description adds no value to the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and resource ('metadata and activity counters for a GitHub repository'), which conveys the core purpose. It is distinguishable from sibling tools that target issues, commits, and workflow runs, though it does not explicitly name those alternatives.
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?
There is no explicit guidance on when to use this tool versus the siblings. The purpose implies it is for repository-level metadata and counters, but the description does not state exclusions or direct the agent to alternatives like get_open_issues or get_recent_commits.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure, but it only restates the basic read function. It does not mention auth requirements, rate limits, what 'recent' means, whether filters apply, or which run conclusions are included.
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?
One concise 10-word sentence with the verb and resource front-loaded. Every word earns its place with no filler or repetition of schema structure.
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?
This is a simple 3-param read tool with an output schema present, so return-shape documentation is unnecessary. However, usage selection guidance and behavioral context (auth, filtering, recency definition) are missing, making it merely adequate for autonomous invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it only hints at the limit parameter via 'recent' and leaves owner/repo completely implicit. An agent gets no explicit guidance on what each parameter controls or how limit interacts with recency.
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 a specific verb ('Get') and resource ('recent GitHub Actions workflow runs') and states what is returned ('their conclusions'). It clearly distinguishes itself from the sibling tools, which target repository info, issues, and commits.
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 when-to-use guidance is provided. The description does not name alternatives like get_open_issues or get_recent_commits, nor does it state conditions under which this tool should be preferred, leaving the agent to infer selection from the tool name alone.
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?
With no annotations, the description carries the behavioral disclosure burden. It usefully reveals that pull requests are excluded, which is a non-obvvious selection behavior. It does not mention pagination, limit handling, sorting, or access requirements, though the output schema covers return shape.
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 front-loads the operation and then adds the important pull-request exclusion. Every word contributes; there is no filler or redundancy.
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?
For a simple read-only issue-listing tool with an output schema present, the description identifies the resource, scope, and exclusion that matter most. The main gaps are explicit usage boundaries and parameter-level documentation, which are already penalized in their respective dimensions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not compensate. It does not explain the role of owner, repo, or limit beyond the schema titles/default. The parameter names are conventional enough to infer, but the description adds no parameter-level meaning.
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 states a specific action ('Get'), resource ('open issues'), and scope ('for a GitHub repository'), plus a critical qualifier ('excluding pull requests'). This clearly distinguishes it from the sibling tools, which cover repository info, commits, and workflow runs rather than issues.
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?
Usage is implied: call this when you need open issues for a GitHub repository. However, there is no explicit when-to-use versus when-not-to-use guidance, and no alternatives such as get_recent_commits are named, so an agent must infer the decision boundary.
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 burden of behavioral clarification. The verb 'get' communicates a read-only operation, and the mention of commits and authors indicates the returned data. However, it does not disclose pagination, ordering, rate limits, auth requirements, or possible failure modes.
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, front-loaded sentence with no filler or redundant information. Every word contributes to identifying the action and the resource.
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 the tool is simple: three scalar parameters, two required, and an output schema exists, so return values do not need to be described. The description plus the input schema is broadly sufficient for an agent to select and invoke the tool, though the exact meaning of 'recent' and the limit parameter is left to inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needs to compensate for the schema's lack of parameter documentation. It provides only a high-level hint that the resource is a GitHub repository and that commits are recent; it does not explain how 'owner', 'repo', or 'limit' should be used or that limit defaults to 10.
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 names a specific operation ('get'), a concrete resource ('recent commits'), and the expected result ('and their authors') for a GitHub repository. This clearly distinguishes it from sibling tools that target repository info, issues, or workflow runs.
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 wording implies this tool should be used when an agent needs commit history and author information, but it never explicitly states when to prefer it over get_repository_info, get_open_issues, or get_workflow_runs. There is no when-not-to-use or alternative guidance, so the usage context is only implied.
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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