github-assistant-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The tools are clearly distinct: one fetches GitHub profile data, while the others handle local workspace file operations (listing, reading, searching, diffing). No functional overlap exists between them.
Naming Consistency5/5All tool names follow a consistent 'verb_noun' pattern (e.g., list_files, read_file, summarize_diff). The single compound name 'get_github_profile' still adheres to the same structure, maintaining a uniform convention.
Tool Count4/5Five tools is a reasonable number for a focused assistant, neither too sparse nor overwhelming. However, the mix leans heavily toward workspace operations rather than GitHub-specific actions, which slightly reduces appropriateness for the server's stated purpose.
Completeness2/5The tool surface is severely incomplete for a GitHub assistant: it only covers profile retrieval and local file operations. Core GitHub workflows like issues, pull requests, repository management, and code search are entirely absent, making the toolset insufficient for its intended domain.
Average 4/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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. However, it only restates the basic function without exposing any behavioral traits: it doesn't mention that it traverses directories, that output includes files and directories (or just files), whether it returns a tree or flat list, or any caveats like permission requirements. This is a significant gap for a tool with no annotations.
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 with no filler. It immediately states the action and purpose, making it highly scannable and efficient.
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 only two parameters, both fully described in the schema, and no output schema. The description states the core purpose and intended usage context, which is sufficient for an agent to know when to invoke it. It doesn't detail return format, but for a listing tool that's often implicit. Overall, it's adequately complete for the tool's simplicity.
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% (both parameters are fully described in the schema), so the baseline is 3. The description adds no parameter-specific details, but the schema already provides defaults and explanation, so the description does not need to compensate. No extra semantic value is added.
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 action ('List files') and the resource ('in the workspace'), with a specific purpose ('so the assistant can inspect the project before reading or summarizing it'). This distinguishes it from sibling tools like read_file (which reads content) and search_context (which searches).
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 explicitly indicates when to use it: 'before reading or summarizing it' – providing a clear usage context. It doesn't explicitly state exclusions or alternatives, but the context is sufficient for an agent to infer it should be used first in a project inspection workflow.
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 behavior. It only states 'Get the public GitHub profile' without mentioning authentication requirements, rate limits, return format, or side effects. The description is minimally transparent beyond the core action.
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 fluff. It states the action and target clearly, earning full marks for conciseness.
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 the tool's simplicity (no parameters, no output schema), the description adequately conveys what it does. It could mention the return format, but the core purpose is clear and complete for the given context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and schema coverage is effectively 100% (vacuously). The description doesn't need to explain parameters, and the baseline for 0-parameter tools is 4. It does not add any misleading parameter info.
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 clearly identifies the resource ('public GitHub profile of imshashwatsingh'). This distinguishes it from sibling tools (file operations), making the purpose unambiguous.
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 context is clear: use when you need the public GitHub profile for the specified user. No explicit exclusions or alternatives are mentioned, but the sibling tools are unrelated, so confusion is unlikely. It lacks explicit 'when not to use' guidance, hence not a 5.
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. 'Read' clearly implies a non-destructive, read-only operation, but the description does not disclose any additional behavioral traits such as error behavior, encoding, or line range semantics (though line range is covered by the schema). It is adequate but minimal, not misleading.
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 two sentences, front-loaded with the core action and followed by a useful usage hint. There is zero filler, and every word earns its place.
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 a well-documented schema. The description provides sufficient context for a basic read operation, including the prerequisite step of listing files. While there is no output schema, the return value (file content) is obvious. Missing details like error handling are minor and expected for such a straightforward 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?
Schema description coverage is 100% for all three parameters (path, startLine, endLine), with clear descriptions. The tool description adds no additional meaning beyond the schema, so the baseline of 3 applies as the schema does the heavy lifting.
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 and resource: 'Read a text file from the workspace.' This is a specific, unambiguous action that distinguishes it from sibling tools like list_files (listing) and search_context (searching). The inclusion of 'text file' also scopes the tool's domain.
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 explicitly instructs to 'Use list_files first to discover available files,' providing clear contextual guidance on when to use this tool relative to the siblings. It implies that read_file is for after discovery, though it does not explicitly state exclusions or alternative scenarios.
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 full burden for behavioral disclosure. It states the core behavior (returns matching files and surrounding lines) but does not mention edge behaviors such as case sensitivity, binary file handling, or ordering of results. It adds value beyond the schema but lacks deeper behavioral context.
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 two sentences, both information-dense with no filler. It immediately states the action, then the result and purpose, making it easy to scan and understand the tool's role.
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?
Despite lacking an output schema and annotations, the description provides a sufficient high-level understanding of the return value. Combined with a fully documented schema, it is complete enough for a straightforward search tool. It could elaborate on return format, but the essentials are present.
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%, so the baseline is 3. The description echoes the 'query' and 'contextLines' concepts ('keyword or phrase', 'surrounding lines') but does not add substantive meaning beyond what the schema parameters already document. It does not clarify path defaults or maxResults behavior beyond schema.
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 ('Search') with a clear resource ('the workspace') and explicitly states the output ('matching files and surrounding lines'). This clearly distinguishes it from sibling tools like list_files and read_file, which serve different purposes.
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 implies when to use it through the clause 'so the assistant can understand relevant context,' indicating it is for gaining situational understanding via keyword search. It does not explicitly mention alternatives or exclusion cases, but for a simple search tool this is adequate context.
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?
No annotations are provided, so the description carries the transparency burden. It clearly signals a read-only operation via 'Inspect' and describes the output shape (summary plus actual diff). It does not detail edge cases such as empty diffs or repository errors, but the core behavioral contract is well communicated.
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 key action and outcome. Every clause adds value, and there is no fluff or redundant repetition of the tool name.
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 correctly explains what the tool returns: changed files, additions, deletions, and the actual diff. The parameters are fully documented in the schema, so the description combined with the schema gives sufficient context for correct selection and invocation.
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 has 100% description coverage for all four parameters, so the baseline is 3. The description adds context about the overall output but does not enrich understanding of individual parameters 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 uses a specific verb ('Inspect') and resource ('current Git diff'), and clearly states it returns a structured summary with changed files, additions, deletions, and the actual diff. This distinguishes it from sibling tools like list_files and read_file, which do not operate on Git diffs.
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 the assistant to summarize' implies the intended use case: obtaining diff data to produce a summary. However, there is no explicit guidance about when to choose this over alternatives or when not to use it, so it relies on implication rather than clear direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
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- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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