@workspacejson/codex-mcp
OfficialServer Quality Checklist
Latest release: v0.1.2
- Disambiguation5/5
Each tool has a distinct and clear purpose: get context for a single file, list fragile files, and assess a set of file changes. There is no ambiguity or overlap.
Naming Consistency5/5All tools follow a consistent 'workspace_verb_noun' pattern in snake_case, making them predictable and easy to understand.
Tool Count5/5Three tools is well-scoped for a specialized server focused on workspace.json code intelligence; each tool earns its place without being too few or too many.
Completeness5/5The tool surface covers the core operations for the domain: inspecting individual file history, listing high-risk files, and validating changes against historical data. No obvious gaps for its intended read-only analysis purpose.
Average 4.5/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 74 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by detailing the two signal types (fragility and co-change) and explaining that 'fragile:false with empty partners' is a valid, non-error response. No contradictions.
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 well-structured with bullet points and clear sections. Every sentence serves a purpose, and the length is appropriate for the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description provides a detailed return JSON structure and explains edge cases (no history, file not indexed). It is complete for a read-only informational 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 coverage is 100% and already describes the path parameter well. The description does not add additional semantics beyond restating the parameter. Baseline 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 returns 'behavioral intelligence' about a single file, combining fragility and co-change signals. It distinguishes itself from siblings like workspace_assess_change and workspace_list_fragile_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 explicitly says 'Call this before editing or creating a file' and provides guidance on interpreting results (minimal changes for fragile, treat co-change partners as candidates). It lacks explicit exclusion of when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) are fully consistent with the description, which adds valuable context: the tool never certifies a change as safe, and it clarifies that 'none' is not a safety approval. The decision semantics are detailed, disclosing the mechanical enforcement logic. No contradictions found.
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 fairly long but well-structured using bullet points for decision semantics and a JSON block for the return value. The purpose is front-loaded in the first sentence. While each sentence adds value, some redundancy exists (e.g., the return JSON is described both in text and in a code block). Still, it remains readable and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description provides a detailed return JSON structure with field descriptions, ensuring the agent understands what to expect. The tool's single parameter is fully covered by schema and description additions. The decision semantics are thoroughly explained, leaving no ambiguity about the tool's behavior.
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 input schema has 100% coverage, describing the paths parameter with type, items, minLength, and maxItems. The description adds context beyond the schema, specifying 'repo-relative or absolute paths' and 'in the proposed change (1-200)', which helps the agent understand acceptable input. However, it does not explain how paths are resolved or validated further.
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: 'Evaluate a SET of file paths…against workspace.json fragility and co-change history, and return a mechanical enforcement decision.' It uses a specific verb ('evaluate') and resource ('changeset'), distinguishing it from sibling tools like workspace_get_file_context (single file context) and workspace_list_fragile_files (listing fragile 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 explains decision semantics and when each applies (e.g., deny, warn, annotate, none). It implies usage for proposed changes but does not explicitly state when not to use or mention alternatives like the sibling tools. The context signals and sibling names provide implicit guidance, but the description could be more explicit about exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses sorting order (most fragile first), default limit, and response format. Annotations already declare readOnlyHint and destructiveHint, so no contradiction. The description adds valuable behavioral context beyond annotations.
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 reasonably concise and front-loaded with the main action. Including the full return JSON is helpful but adds length; still, every sentence serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description provides the complete return format and key behavioral details. Given the simple parameters and clear annotations, the description is sufficiently complete for accurate 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?
Schema coverage is 100% for the single parameter 'limit', with default and maximum defined. The description restates these but adds no new semantic meaning beyond what the schema 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 clearly states the tool lists fragile files from workspace.json sorted by score. It distinguishes from sibling tools like workspace_assess_change, workspace_get_cochange_partners, and workspace_get_file_context by focusing on a specific subset of files.
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?
Explicit usage guidance is provided: 'Use for orientation at the start of a task: it tells you which parts of the codebase carry the most historical risk.' This tells when it should be used and its purpose.
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/workspace-json/codex-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server