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cantbeblank96

qodercli-mcp

by cantbeblank96
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.4.2

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: ask-qoder for delegating tasks, list-sessions for managing sessions, and list-models for model selection. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (ask-qoder, list-sessions, list-models), making them predictable and easy to understand.

    Tool Count5/5

    Three tools is appropriate for a CLI wrapper MCP server, covering the core interactions (task execution, session management, model listing) without unnecessary bloat.

    Completeness5/5

    The tool set covers the essential workflows for qodercli: initiating tasks, resuming sessions, and selecting models. No obvious gaps for its intended purpose.

  • Average 4.2/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 20 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

  • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior3/5

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

    No annotations provided; description explains it delegates to a local coding assistant and returns session_id for resumption, but does not disclose potential side effects like file modifications or shell access, leaving that to schema parameter descriptions.

    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?

    Three concise sentences that front-load the core action, use cases, and the session/resume flow; no wasted words.

    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?

    For a 13-parameter tool with output schema, the description provides the essential high-level context (delegation, use cases, resume flow) but could mention prerequisites like listing models first; schema compensates for parameter details.

    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 covers all 13 parameters with descriptions; the description adds no parameter syntax or format details beyond schema, so baseline 3 applies.

    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 uses a specific verb ('Delegate') and resource ('qodercli'), lists concrete use cases (second opinion, code review, coding task), and clearly distinguishes from sibling tools that list sessions/models.

    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?

    Clearly explains when to use (second opinion, code review, self-contained coding task) but doesn't mention when not to use or alternatives beyond implicit distinction from list tools.

    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?

    No annotations are provided, so the description carries the burden. It states it lists models but does not disclose any behavioral traits such as read-only nature, authentication, or caching. However, the tool is simple and likely read-only, so the lack of disclosure is not critical but could be improved.

    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 concise with two sentences, front-loading the purpose. The second sentence adds clear usage guidance. No fluff.

    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 that there are no parameters and the tool is simple, the description is complete enough. It tells the agent what the tool does and when to use it. An output schema is present but not detailed in the description; however, for a list operation, the description is adequate.

    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?

    There are no parameters, and schema description coverage is 100% (vacuously). The description does not need to add parameter meaning. Baseline for zero parameters is 4, and the description adds no unnecessary information about parameters.

    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 clearly states what the tool does: 'List models currently supported by qodercli.' It uses a specific verb ('List') and resource ('models supported by qodercli'). It also distinguishes from siblings by noting to use this before picking a model name for ask-qoder, implying ask-qoder is a different action.

    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?

    The description explicitly states when to use this tool: 'Use this before picking a model name for ask-qoder.' This gives clear context. It does not explicitly mention when not to use it, but given the tool's singular purpose, the guidance is sufficient.

    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?

    No annotations are provided, so the description carries the full burden. It accurately describes a read-only listing operation with no side effects, and adds the context that sessions are 'local' (client-side). For a simple tool with no parameters, this is adequate 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence of 14 words, front-loaded with the action and purpose. Every word earns its place; there is no redundancy or fluff.

    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?

    Given the tool's simplicity (zero parameters, presence of an output schema), the description is fully sufficient. It explains what the tool does, why it is used, and the sibling tools are simple. The output schema covers return values, and the description previews the key fields.

    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?

    The tool has no parameters, and schema description coverage is trivially 100%. Per the guidelines, zero parameters justifies a baseline score of 4. The description does not need to add parameter information.

    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 clearly states the verb 'list', the resource 'local qodercli sessions', and the specific output fields (index + id + summary). It also explains the purpose: to pick a resume_session_id for ask-qoder, which distinguishes it from its siblings (ask-qoder and list-models).

    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?

    The description explicitly says 'so you can pick a resume_session_id for ask-qoder', which tells the agent when to use this tool (before calling ask-qoder with a session ID). It does not mention when not to use it or provide alternatives, but the context is clear and sufficient for a simple list tool.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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