Review MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation4/5
The tools are mostly distinct with clear boundaries: check_cli_status verifies CLI availability, while review_code, review_directory, and review_file handle code reviews at different granularities. However, review_directory and review_file could be slightly confused as both target files, but their descriptions clarify the scope difference (directory vs. single file).
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case: check_cli_status, review_code, review_directory, and review_file. This predictability makes it easy for an agent to understand and select tools without confusion.
Tool Count5/5With 4 tools, the server is well-scoped for its purpose of code review CLI integration. Each tool earns its place by covering distinct aspects: checking availability and reviewing code at different levels (string, file, directory). This count is neither too thin nor excessive.
Completeness4/5The tool surface covers the core workflows for code review using CLIs: checking availability and performing reviews. A minor gap exists in not providing tools to manage or configure the CLIs (e.g., setting API keys), but agents can work around this as the essential review operations are fully supported.
Average 3.7/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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
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 mentions that the tool returns feedback from reviewers for Claude to consider, but lacks details on permissions, rate limits, error handling, or what the feedback format entails. For a tool that interacts with external CLIs and returns results, 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that efficiently state the tool's purpose and outcome. It's front-loaded with the main action and avoids unnecessary details, though it could be slightly more structured by explicitly separating purpose from usage context.
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?
Given the tool's complexity (interacting with multiple CLIs, returning feedback), lack of annotations, and no output schema, the description is moderately complete. It covers the basic purpose and outcome but misses behavioral details like authentication, error cases, or feedback structure. It's adequate for a minimal understanding but has clear gaps for effective agent 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%, so the schema already documents all parameters (filePath, context, reviewers) with descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or constraints. Baseline score of 3 is appropriate when the schema handles parameter documentation adequately.
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 clearly states the tool's purpose: 'Request a code review of a specific file from Codex and Gemini CLIs.' It specifies the verb ('request a code review'), resource ('specific file'), and reviewers ('Codex and Gemini CLIs'), but doesn't explicitly distinguish it from sibling tools like 'review_code' or 'review_directory', which likely have different scopes.
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 by mentioning 'Returns feedback from both reviewers for Claude to consider,' suggesting it's intended for Claude to process feedback. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'review_code' or 'review_directory,' nor does it specify prerequisites or exclusions, leaving the context somewhat vague.
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 full burden. It discloses that the tool requests reviews from external CLIs and returns feedback, but doesn't mention authentication needs, rate limits, error conditions, or what happens if the directory doesn't exist. For a tool interacting with external services, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that efficiently convey purpose and outcome. It's front-loaded with the core functionality. The second sentence about Claude integration could be slightly more integrated, but overall it's well-structured with minimal waste.
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?
Given 3 parameters with full schema coverage but no annotations or output schema, the description provides adequate purpose but lacks behavioral context for a tool that interacts with external CLIs. It doesn't explain the format or structure of the returned feedback, which is important since there's no output schema.
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 schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
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 specific action ('Request a code review'), target resource ('all files in a directory'), and tools involved ('Codex and Gemini CLIs'). It distinguishes from sibling tools like 'review_file' (single file) and 'review_code' (unclear scope) by specifying directory-level review.
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 directory-level code reviews but doesn't explicitly state when to use this tool versus alternatives like 'review_file' or 'review_code'. It mentions 'for Claude to consider' which suggests integration context, but lacks clear when/when-not guidance or prerequisite conditions.
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. It discloses that the tool requests reviews from specific CLIs (Codex and Gemini) and returns feedback from both reviewers, but lacks details on permissions, rate limits, error handling, or what 'feedback' entails. It adds some behavioral context but leaves gaps for a mutation-like operation.
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 appropriately sized and front-loaded with key information in two concise sentences. Every sentence earns its place by stating the action, input method, and output purpose without redundancy.
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?
Given no annotations, no output schema, and 3 parameters with full schema coverage, the description is moderately complete. It covers the basic operation and output intent but lacks details on behavioral traits like error cases or feedback format, which are important for a tool that interacts with external CLIs.
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 schema already documents all parameters. The description adds minimal value beyond the schema by implying the 'code' parameter is provided as a string and 'reviewers' defaults to 'both', but does not elaborate on parameter interactions or usage nuances.
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 specific verbs ('Request a code review') and resources ('from Codex and Gemini CLIs'), and distinguishes it from siblings by specifying it reviews code provided as a string (unlike review_directory or review_file which likely handle files/directories).
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 provides clear context for when to use this tool ('Provide code directly as a string'), but does not explicitly state when not to use it or name alternatives like review_directory or review_file. It implies usage for string-based code review without file system access.
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 of behavioral disclosure. It clearly indicates this is a read-only check operation without side effects, but doesn't specify what format the availability information will be returned in, whether there are authentication requirements, or what happens if no CLIs are found. The description adds basic behavioral context but lacks detail about the output format.
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 perfectly concise with two focused sentences. The first sentence states the purpose, the second provides usage guidance. Every word earns its place, and the information is front-loaded with the core functionality stated immediately.
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 zero-parameter tool with no annotations and no output schema, the description provides good context about what the tool does and when to use it. However, it doesn't describe the return format or what specific information will be provided about CLI availability, leaving some ambiguity about the tool's output.
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?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on the tool's purpose and usage context.
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 specific action ('Check which code review CLIs are installed and available') and identifies the resources (Codex/OpenAI and Gemini CLIs). It distinguishes this tool from its siblings (review_code, review_directory, review_file) by focusing on CLI availability checking rather than performing reviews.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use this before requesting reviews to see what's available.' This clearly indicates when to use this tool (as a prerequisite check) versus when to use its sibling review tools, establishing a clear workflow relationship.
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.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/je4550/review-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server