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j0hanz

cortex-mcp

by j0hanz
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Server Quality Checklist

67%
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  • Latest release: v1.11.0

  • Disambiguation5/5

    With only one tool, there is no risk of confusing it with other tools. The tool's purpose is clearly defined as structured multi-step reasoning.

    Naming Consistency5/5

    The single tool name 'reasoning_think' follows a clear verb_noun pattern (verb 'think' with noun 'reasoning' as modifier), and consistency is not an issue with only one tool.

    Tool Count3/5

    One tool is minimal but appropriate given the focused domain of reasoning. The tool is complex with multiple levels and modes, so it earns its place, but the count feels slightly thin for a general MCP server.

    Completeness5/5

    The tool covers the full lifecycle of multi-step reasoning: start, continue, complete, with support for different levels and modes. No obvious gaps for a reasoning-only server.

  • Average 4.8/5 across 1 of 1 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
    • Last stable release on
    • 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.

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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

  • Behavior5/5

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

    Annotations are all false, and the description adds extensive behavioral context: thought is stored verbatim, sessionId must be passed on continuation, levels have step and budget ranges, and the summary field contains the next call. No contradictions with annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

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

    The description is well-structured with sections (usage pattern, levels, alternatives, errors) and front-loaded with the main purpose. It is fairly long but each section earns its place. Slightly verbose, but still efficient.

    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 complexity (12 parameters, 100% schema coverage, output schema exists), the description covers all necessary aspects: workflow, levels, budgets, errors, and alternatives. It is complete for an agent to correctly select and 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 100%, so baseline is 3. However, the description adds significant workflow context (e.g., usage of sessionId, thought, stepSummary, and the difference between step and batch modes) that enhances understanding beyond the schema. This justifies a score of 4.

    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 it is a 'Structured multi-step reasoning tool' and explains it decomposes analysis into sequential thought steps stored in a persistent session trace. There are no sibling tools, so differentiation is not needed. The purpose is specific and unambiguous.

    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?

    The description provides a detailed usage pattern with start/continue/repeat steps, explicitly mentions alternatives like runMode='run_to_completion' and structured fields (observation/hypothesis/evaluation), and lists error codes. This gives clear guidance on when to use the tool and how to proceed.

    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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