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dazebug

easy-codex-mcp

by dazebug
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Python
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one starts a new conversation and returns a thread_id, the other continues an existing conversation using that thread_id. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern with snake_case, using 'start_new_' and 'continue_' as clear action prefixes. This consistency makes the API predictable and easy to navigate.

    Tool Count4/5

    At only two tools, the count is on the low end but perfectly appropriate for the server's narrow purpose of managing Codex conversations. The tools cover the essential start/continue workflow without unnecessary additions.

    Completeness4/5

    The tool surface covers the primary conversation lifecycle (start and continue) with no dead ends. Minor gaps such as the absence of explicit conversation termination or history listing are acceptable given the read-only, session-based scope.

  • Average 4.6/5 across 2 of 2 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

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  • This repository includes a README.md file.

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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

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the full burden. It discloses context resumption via thread_id, read-only sandbox limitations, and the return format. The detailed sandbox limitations are delegated to the sibling tool rather than spelled out, which is a minor gap.

    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?

    Well-structured with an opening statement, context, use cases, Args, and Returns sections. Content is organized and front-loaded; the slight redundancy between 'Continue' and 'Resumes' is minor and does not detract from effectiveness.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given 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 covers purpose, usage, parameters, and return value. It could mention error handling for invalid thread_id, but for a conversational continuation tool, the provided information is sufficient for an agent to use it correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, but the description provides an Args section explaining each parameter clearly: thread_id from previous conversation, prompt with @filepath mention, and working_directory as optional. This fully compensates for the schema's lack of descriptions.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Description explicitly states 'Continue an existing Codex conversation' with a specific verb and resource. It distinguishes from sibling start_new_conversation by emphasizing thread_id-based resumption and referencing the sibling's sandbox limitations.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    A dedicated 'When to use' section lists three concrete scenarios for using this tool. However, it lacks an explicit 'when not to use' clause and only indirectly references start_new_conversation as an alternative, so it doesn't fully meet the 5-level bar.

    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?

    With no annotations, the description carries the full burden. It clearly discloses read-only sandbox mode, inability to modify files/execute commands, and returns a thread_id and response. The description adds context beyond the basic schema, though it omits potential auth/error details.

    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 well-structured with clear sections (main description, limitations, when to use, args, returns). It is front-loaded with the core purpose, and every bullet point adds value without excessive verbosity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite no output schema or annotations, the description explains the return format (Dict with thread_id and response), lists limitations, and provides use cases. It is sufficiently complete for an AI agent to understand when and how to invoke the tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, but the description compensates by explaining each parameter: prompt includes the useful '@filepath' syntax hint, and working_directory is described as 'Directory path for codex to work in.' This adds meaning beyond the schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states a specific verb and resource: 'Start a new conversation with OpenAI Codex CLI.' It clearly distinguishes from the sibling tool continue_conversation by mentioning it returns a thread_id for continuing the conversation later.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Provides explicit 'When to use' list with four concrete use cases and a 'Limitations' section that explicitly excludes write operations and shell commands, saying 'Use only for read-only tasks.' This is strong guidance on when to use and when not to use.

    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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