mcp-github-explorer-server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool targets a distinct aspect of GitHub data: profile info, top repositories, and language distribution. No overlap in purpose, making selection unambiguous.
Naming Consistency5/5All tool names follow the verb_noun pattern with snake_case: get_github_profile, list_top_repos, get_language_stats. Consistent and predictable.
Tool Count5/5Three tools is a well-scoped set for a focused GitHub explorer server. Each tool serves a clear function without redundancy or bloat.
Completeness4/5The server covers common GitHub exploration needs: profile, top repos, and language stats. Minor gaps like detailed repo info or follower lists exist but are not critical for the apparent purpose.
Average 3.9/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool accesses public information and lists the returned fields, but it does not disclose error handling, rate limits, or confirmation that it is read-only. This is adequate but leaves some gaps.
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, focused sentence that front-loads the core purpose and lists the output fields efficiently. Every word contributes meaning, and it is appropriately sized for the tool's simplicity.
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 profile lookup tool with one parameter and no output schema, the description is sufficiently complete by enumerating the returned data fields. It adds context that is not present in the schema, though it could mention behavior for nonexistent users or error conditions.
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 provides full documentation for the single 'username' parameter with a clear description and example. The tool description does not add additional parameter behavior or syntax beyond what the schema covers, so the baseline score 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 searches public GitHub user or organization information, and enumerates the specific fields returned (name, bio, company, location, followers, public repos). This distinguishes it from sibling tools like list_top_repos and get_language_stats, which focus on repositories and language statistics.
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 description implies usage for fetching profile data, but it does not explicitly mention when to use this tool over alternatives. No exclusion criteria or alternatives are provided, though the scoped description makes the use case fairly evident.
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 provided, the description carries the full burden for behavioral disclosure. It specifies that it returns public repositories with main language and star count, which is useful, but it does not mention pagination, rate limits, authentication, or error behavior. The description adds some context but is not rich.
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, effective sentence that is front-loaded with the verb 'Lista' and clearly communicates the tool's purpose. It contains no unnecessary words or repetition, making it both concise and well-structured.
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 list tool with 2 parameters and full schema coverage, the description is adequate. It states the output fields (language and star count) and implies a list structure, though it lacks an explicit output schema. It does not mention potential edge cases like user not found, but overall it provides enough context for typical use.
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 both 'username' and 'limit' parameters having descriptive text. The tool description does not add additional parameter meaning beyond what the schema already provides, so a baseline score 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 it lists the most starred public repositories of a GitHub user with language and star count, which is a specific verb+resource+scope. It distinguishes itself from sibling tools like get_github_profile and get_language_stats by focusing on repositories rather than profile or language statistics.
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 description implies its usage (for listing a user's top repos) but provides no explicit guidance on when to use this tool versus alternatives like get_github_profile or get_language_stats. It does not mention any exclusions or prerequisites, so it offers only implied usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behavior: it only includes public non-fork repositories and uses the main language of each repo. This goes beyond the schema, though it doesn't describe edge cases (e.g., repos without a language) or return format specifics.
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 one concise sentence, front-loaded with the main action and object. Every phrase earns its place—no redundant details or filler.
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 1-parameter tool with no output schema, the description provides adequate details on scope and method. It could mention whether the result is a list/map or whether it includes all languages, but the core purpose is clear.
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 coverage is 100% for the single 'username' parameter. The description adds no new meaning beyond the schema, which already describes it as a GitHub username (login). Baseline of 3 applies.
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 verb ('Calcula' / calculates), a precise resource (percentage distribution of programming languages in public non-fork repositories of a GitHub user), and the methodology (based on each repo's primary language). This clearly differentiates it from siblings like get_github_profile and list_top_repos.
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?
It gives clear context: use when you need language distribution for a user's public, non-fork repos. It does not explicitly mention when not to use it or name alternatives, but the distinct purpose makes usage straightforward.
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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