AI Code Review MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
The tools are mostly distinct with clear purposes, but there is some potential overlap between fs_list_files and fs_search_files, as both involve listing files, which could cause minor confusion. However, their descriptions clarify that one lists all files while the other searches by pattern, helping to mitigate ambiguity.
Naming Consistency3/5
The naming is mixed, with fs_* tools using a prefix and github_* tools using a different prefix, but within each group, the verb_noun pattern is consistent (e.g., fs_list_files, github_get_pr). This mixed convention reduces overall consistency, though it remains readable and functional.
Tool Count4/5
With 7 tools, the count is reasonable for an AI code review server, covering file system operations and GitHub PR interactions. It is slightly lean but well-scoped, as each tool serves a specific purpose without obvious bloat or redundancy.
Completeness3/5
The tool surface has notable gaps for a code review domain, such as missing tools for creating or updating PRs, commenting on code, or analyzing code quality directly. While it covers basic file and PR retrieval, it lacks full lifecycle coverage, which could limit agent effectiveness in comprehensive code review workflows.
Average 2.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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. 'Get pull request details' implies a read-only operation, but it doesn't specify authentication needs, rate limits, error handling, or what the return format includes (e.g., JSON structure). For a tool with no annotation coverage, this is a significant gap in transparency.
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 'Get pull request details' is a single, efficient sentence that is front-loaded and wastes no words. It directly states the purpose without unnecessary elaboration, making it highly concise and well-structured for its simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a read operation with 3 parameters) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' are returned, potential errors, or behavioral aspects like permissions. For a tool with no structured output or safety hints, more context is needed to be adequately complete.
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 input schema has 100% description coverage, with clear documentation for 'owner', 'repo', and 'pr_number'. The description adds no additional meaning beyond the schema, such as examples or constraints. According to the rules, with high schema coverage (>80%), the baseline is 3, which is appropriate here 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get pull request details' states a clear verb ('Get') and resource ('pull request details'), which is adequate. However, it doesn't distinguish this tool from its sibling 'github_get_pr_diff' (which likely gets PR differences) or 'github_list_prs' (which lists PRs), making it vague in comparison. It avoids tautology but lacks specificity about what 'details' encompass.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'github_get_pr_diff' for diff details or 'github_list_prs' for listing PRs, nor does it specify prerequisites or contexts for usage. This leaves the agent without explicit or implied direction.
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 the full burden of behavioral disclosure. It states the tool lists files, implying a read-only operation, but doesn't specify whether it's recursive, includes hidden files, handles errors, or provides pagination. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and efficiently communicates the core functionality, making it easy for an agent to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is too minimal for a tool that interacts with a file system. It doesn't cover behavioral aspects like error handling, output format, or limitations (e.g., recursion depth), leaving the agent with incomplete context for reliable use.
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 input schema has 100% description coverage, with the 'path' parameter fully documented in the schema itself (including default value and meaning). The description doesn't add any parameter details beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('files in a directory'), making the tool's purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'fs_search_files', which might also list files but with different functionality. The description is specific about what it does but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'fs_search_files' or other file-related siblings. It doesn't mention any prerequisites, exclusions, or contextual cues for selection, leaving the agent to infer usage based solely on the tool name and basic description.
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. It states the action ('Read') but doesn't cover critical traits like whether it requires specific permissions, handles binary vs. text files, has size limits, or what happens on errors (e.g., missing files). This leaves significant gaps for a file operation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste: 'Read file contents from workspace'. It is front-loaded and directly conveys the core action without unnecessary details, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of file reading (potential for errors, format issues, etc.), no annotations, and no output schema, the description is incomplete. It doesn't address return values, error conditions, or behavioral nuances, leaving the agent under-informed for reliable tool 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 input schema has 100% description coverage, with the 'path' parameter clearly documented as 'File path relative to workspace'. The description adds no additional meaning beyond this, as it doesn't elaborate on path formats, restrictions, or examples. Given the high schema coverage, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Read') and resource ('file contents from workspace'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'fs_list_files' and 'fs_search_files' by focusing on reading file contents rather than listing or searching. However, it doesn't specify what format the contents are returned in or any limitations, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'fs_read_file' over 'fs_search_files' for content retrieval or any prerequisites like file existence or permissions. Without such context, the agent must infer usage 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.
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. It mentions the action ('Search for files') but doesn't cover key traits like whether it's read-only, if it has rate limits, what permissions are needed, or how results are returned (e.g., format, pagination). This leaves significant gaps for a tool with potential file system access.
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, efficient sentence with zero waste, front-loading the core purpose and including helpful examples. Every word earns its place, making it appropriately sized and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (file system search with two parameters) and lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like safety, performance, or result format, which are crucial for an AI agent to use it correctly in context with sibling tools.
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 schema description coverage is 100%, so the input schema already documents both parameters ('pattern' and 'path') with descriptions and defaults. The description adds minimal value by providing examples (e.g., '*.js, *.py') but doesn't explain semantics beyond what the schema provides, aligning with the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Search for files by pattern' with examples, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'fs_list_files' (which might list files without pattern matching), so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as 'fs_list_files' for listing all files without pattern matching or other sibling tools for different contexts. It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.
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. It states the action ('Get pull request diff') but doesn't mention whether this is a read-only operation, requires authentication, has rate limits, returns raw diff text or structured data, or any error conditions. This leaves significant gaps for an agent to understand how to invoke it correctly.
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, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of GitHub operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the diff output looks like (e.g., patch format, file list), potential side effects, or error handling, which are crucial for an agent to use this tool effectively in code review scenarios.
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 input schema has 100% description coverage, clearly documenting all three required parameters (owner, repo, pr_number). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does 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 ('pull request diff'), and the context 'for code review' adds helpful intent. However, it doesn't explicitly differentiate from sibling tools like 'github_get_pr', which might also be used for code review purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'github_get_pr' or 'github_list_prs'. It mentions 'for code review' which implies a context but doesn't specify prerequisites, exclusions, or comparative use cases with sibling tools.
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. It only states the basic action without covering critical aspects like pagination behavior, rate limits, authentication requirements, or what the output format looks like (e.g., list of PR objects with fields). This is inadequate for a tool that likely returns complex data.
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, efficient sentence that directly states the tool's function without any unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain behavioral traits like pagination or rate limits, nor does it describe the return values (e.g., what fields PR objects include). For a list operation that likely returns structured data, this leaves significant gaps for the 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?
The input schema has 100% description coverage, with clear documentation for all three parameters (owner, repo, state including enum and default). The description adds no additional parameter information beyond what's in the schema, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('pull requests in a repository'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'github_get_pr' (which retrieves a specific PR) or 'github_list_repos' (which lists repositories instead of PRs), preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention scenarios like filtering by state, comparing to 'github_get_pr' for single PR details, or using 'github_get_pr_diff' for diff information. This leaves the agent without context for tool selection.
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 burden. It mentions authentication requirement ('for authenticated user'), which is useful, but doesn't disclose other behavioral traits like pagination handling (implied by per_page param), rate limits, error conditions, or return format. For a list operation with zero annotation coverage, this is insufficient.
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?
Single sentence, zero waste, front-loaded with core purpose. Every word earns its place without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a list operation that likely returns structured data, the description is incomplete. It lacks details on return format (e.g., list of repo objects), pagination behavior (beyond per_page param), or error handling, leaving significant gaps for agent usage.
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% (per_page is fully documented in schema), so baseline is 3. The description adds no parameter-specific information beyond what the schema provides, but doesn't need to compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('GitHub repositories'), specifying scope ('for authenticated user'). It distinguishes from siblings like github_get_pr (which fetches specific PRs) but doesn't explicitly differentiate from github_list_prs (which lists PRs, not repos).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like fs_list_files (for local files) or github_list_prs (for PRs). The description implies it's for listing repositories, but lacks explicit when/when-not instructions or prerequisites beyond authentication.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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