Codex MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation2/5
The tools have significant overlap in purpose, as all four are focused on code review with Codex. While there are distinctions between reviewing a file, project, or code changes, the boundaries are unclear—for example, 'review_code_changes' and 'review_file' could easily be confused for similar tasks, and the descriptions do not fully resolve this ambiguity.
Naming Consistency5/5The tool names follow a highly consistent verb_noun pattern throughout (e.g., continue_review, review_code_changes, review_file, review_project). All use snake_case and start with a verb related to reviewing, making them predictable and easy to parse.
Tool Count4/5With 4 tools, the count is reasonable for a code review server, though it feels slightly thin. Each tool appears to serve a distinct niche (e.g., file vs. project review), but the set might benefit from additional operations like summarization or feedback integration to enhance completeness.
Completeness3/5The server covers core review scenarios (file, project, code changes, and session continuation), but there are notable gaps. For example, it lacks tools for managing review sessions (e.g., starting or ending reviews), handling non-code artifacts, or providing post-review actions like approval or rejection, which could limit agent workflows in broader contexts.
Average 3.3/5 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 Codex will '分析代码,执行必要的编辑或命令,并返回修复摘要' (analyze code, perform necessary edits or commands, and return a fix summary), which implies mutation and output format. However, it lacks details on permissions, rate limits, error handling, or what '修复摘要' (fix summary) entails. For a tool with potential edits and no annotation coverage, this is insufficient 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded: the first states the core purpose, and the second provides usage timing. There's minimal waste, though the second sentence could be more concise. It efficiently conveys key information without redundancy, earning a high score for structure and brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involves Codex agent analysis and potential edits), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral traits like mutation effects, error cases, or output details. While it mentions the tool's role in a workflow, it fails to provide sufficient context for safe and effective use, especially for a tool with siblings and no structured safety hints.
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 three parameters (content, session_id, context) with good descriptions. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, formats, or examples. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
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: '将代码或文本发送给 Codex 代理进行审查、修改和验证' (send code or text to Codex agent for review, modification, and verification). It specifies the verb (send/review) and resource (code/text), but doesn't explicitly differentiate from sibling tools like 'review_file' or 'review_project', which likely have overlapping functions. The description is specific about what happens (Codex analyzes, edits, returns summary) but lacks sibling distinction.
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 provides implied usage guidance: '每次 Gemini 修改代码后,应该调用此工具获取 Codex 的审查意见' (after Gemini modifies code, this tool should be called to get Codex's review). This suggests it's for post-modification review, but doesn't explicitly state when to use it versus alternatives like 'continue_review' or 'review_file'. There's no mention of prerequisites, exclusions, or clear context for choosing among siblings, leaving usage 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?
No annotations are provided, so the description carries full burden. It mentions the tool continues discussions with Codex but doesn't disclose behavioral traits like whether this requires specific permissions, how it handles session state, response format, or any rate limits. The description adds minimal behavioral context beyond the basic 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 two concise sentences. The first sentence states the core purpose, and the second provides usage context, with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool that interacts with an AI system (Codex). It lacks details on response behavior, error handling, or session management, which are critical for contextual understanding.
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 both parameters (message and session_id). The description doesn't add meaning beyond what the schema provides, such as examples of message content or session_id format. Baseline 3 is appropriate when the schema does the heavy lifting.
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: '在现有会话中继续讨论' (continue discussion in an existing session) with specific verbs and resources. It distinguishes from siblings by focusing on continuing existing sessions rather than initiating new reviews, though it doesn't explicitly name the sibling tools for comparison.
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 provides implied usage context: '用于追问 Codex 更多细节或请求进一步的修改建议' (used to ask Codex for more details or request further modification suggestions). However, it doesn't explicitly state when to use this tool versus the sibling tools (review_code_changes, review_file, review_project) or provide clear exclusions.
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. It mentions 'Codex 将分析项目结构和代码,根据提供的变更描述进行审查和修复' (Codex will analyze project structure and code, review and fix based on the provided change description), which implies mutation behavior but lacks details on permissions, side effects, rate limits, or what '修复' (fix) entails. For a tool with potential code modifications, this is insufficient behavioral disclosure.
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 sized with three sentences that are front-loaded with core functionality. Each sentence adds value: the first states the purpose, the second provides usage context, and the third explains the behavioral process. There's minimal waste, though it could be slightly more concise.
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 complexity (code review/fix tool with no annotations and no output schema), the description is moderately complete. It covers purpose and usage well but lacks details on behavioral traits like safety, output format, or error handling. Without annotations or output schema, more context on what '修复' (fix) means and the result format would improve completeness.
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 three parameters. The description adds no additional parameter semantics beyond what's in the schema. The baseline is 3 since the schema provides comprehensive parameter details, and the description doesn't compensate or add value here.
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: '审查整个项目的代码' (review the entire project's code) with specific verbs like '分析项目结构和代码' (analyze project structure and code) and '进行审查和修复' (perform review and fix). It distinguishes from siblings by mentioning '跨文件夹修改、系统级重构或需要全面上下文的情况' (cross-folder modifications, system-level refactoring, or situations requiring comprehensive context), though it doesn't explicitly name alternatives.
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: '适用于跨文件夹修改、系统级重构或需要全面上下文的情况' (suitable for cross-folder modifications, system-level refactoring, or situations requiring comprehensive context). It implies usage scenarios but doesn't explicitly state when not to use it or name specific alternatives like 'review_file' for single-file reviews.
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. It mentions that 'Codex 将读取文件并根据变更描述提供审查建议' (Codex will read the file and provide review suggestions based on the change description), which gives some behavioral insight (e.g., it involves reading and AI analysis). However, it lacks details on permissions, rate limits, output format, or error handling. For a tool with no annotations, this is a significant gap in behavioral disclosure.
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 concise and well-structured in two sentences. The first sentence states the purpose and usage context, and the second explains the behavioral process. Every sentence adds value without redundancy, making it easy to parse and front-loaded with key information.
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 and no output schema, the description provides basic purpose and usage but lacks details on behavioral traits, output format, or error handling. It's adequate for a simple review tool but incomplete for guiding an AI agent fully, especially without annotations to cover safety or operational aspects.
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 three parameters (file_path, change_description, session_id) with clear descriptions. The description doesn't add any parameter-specific semantics beyond what's in the schema, such as examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
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: '审查单个文件的内容' (review content of a single file) and '提供审查建议' (provide review suggestions). It specifies the verb (review), resource (file content), and scope (single file). However, it doesn't explicitly differentiate from sibling tools like 'review_code_changes' or 'review_project' beyond mentioning it's for '改动仅在单个或两个文件内的情况' (changes limited to one or two files).
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: '适用于改动仅在单个或两个文件内的情况' (suitable when changes are limited to one or two files). It implies alternatives by mentioning this scope, but doesn't explicitly name sibling tools or state when not to use it. The guidance is helpful but could be more explicit about comparisons.
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/SpongeBaby-124/CodexReviewerMCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server