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harshpreet931

Advanced Cognitive MCP Server

by harshpreet931
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TypeScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility for confusion between tools.

    Naming Consistency5/5

    With a single tool, naming consistency is inherently perfect; the name 'cognitive_reasoning' is clear and follows a standard pattern.

    Tool Count2/5

    A single tool is too few for a server claiming 'advanced cognitive' capabilities; the tool is overloaded with many diverse features that would benefit from modularization into separate tools.

    Completeness4/5

    The single tool covers a wide range of reasoning modes, personas, and meta-cognitive features, but bundling everything into one tool limits the ability to add or expose specialized capabilities separately.

  • Average 4/5 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
    • 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 full burden. It details multi-modal reasoning, collaborative personas, dynamic knowledge graph, meta-cognitive reflection, and advanced features like parallel processing. However, it omits limitations, error behavior, response format, and cost implications, leaving some gaps.

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

    Conciseness3/5

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

    The description is excessively long with bullet points and sections. While it is front-loaded with the main purpose, the extensive detail on each mode and feature could be condensed. The structure is clear but not concise.

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

    Completeness3/5

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

    Given the tool's complexity and 8 parameters, the description covers usage, modes, and features well. However, there is no output schema, and the description does not specify what the tool returns or how responses are structured, leaving a completeness gap.

    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 coverage is 100% with descriptions for all 8 parameters. The description adds value by explaining each reasoning mode, persona, meta_reflection, and other parameters in narrative form, providing context beyond schema definitions.

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

    Purpose4/5

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

    The description clearly states it is an advanced cognitive processing tool for multi-modal reasoning, knowledge graphs, and collaborative thinking. It lists specific reasoning modes and personas, but the purpose is somewhat buried in verbose marketing language, reducing clarity.

    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 explicitly provides a 'When to use this tool' section listing complex multi-faceted problems, creative ideation, strategic planning, etc. It also mentions automatic selection of modes and personas. However, no sibling tools exist for comparison, so alternative guidance is not applicable.

    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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