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moyu6027

DeepSeek MCP Server

by moyu6027
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Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool named 'reason', there is no possibility of ambiguity or overlap with other tools. The tool has a single, clearly defined purpose of processing queries through DeepSeek's reasoning engine.

    Naming Consistency5/5

    A single tool cannot demonstrate inconsistency in naming patterns. The tool name 'reason' follows a clear verb-based convention that directly describes its function.

    Tool Count2/5

    One tool is too few for a server that appears to interface with a complex reasoning engine. While the tool is well-described, a single tool surface severely limits the server's capabilities and suggests an incomplete implementation for the domain.

    Completeness2/5

    The server has a significant gap in functionality. A reasoning engine server should offer more than just query processing - there are no tools for configuration, status checking, result formatting options, or other typical operations expected from such a service.

  • Average 3.4/5 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

  • 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

  • Behavior2/5

    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 mentions the tool 'leverages advanced reasoning capabilities' and outputs formatted text, but fails to disclose critical behavioral traits such as rate limits, error handling, authentication requirements, or performance characteristics. The description adds some context about the reasoning engine but leaves significant gaps for a tool with potential computational costs.

    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 appropriately sized and front-loaded, with the core purpose stated in the first sentence. Additional sentences provide useful context about the reasoning engine and output formatting. There is minor redundancy in mentioning 'V3 or Claude' twice, but overall, it's efficient and well-structured.

    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 the tool's moderate complexity (1 parameter with nested structure), no annotations, and an output schema that exists (though not detailed here), the description is reasonably complete. It explains the purpose, parameter semantics, and output format, though it could improve by addressing behavioral aspects like error cases or integration specifics.

    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 description adds substantial meaning beyond the input schema, which has 0% description coverage and only specifies a generic object. It details that the 'query' parameter is a dict with 'context' (optional background) and 'question' (specific question) keys, clarifying the expected structure and semantics. This compensates well for the schema's lack of documentation.

    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 the tool's purpose: 'Process a query using DeepSeek's R1 reasoning engine and prepare it for integration with DeepSeek V3 or claude.' It specifies the verb ('process'), resource ('query'), and technology ('DeepSeek's R1 reasoning engine'), but since there are no sibling tools, it cannot demonstrate differentiation from alternatives.

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

    Usage Guidelines3/5

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

    The description implies usage by mentioning integration with 'V3 or Claude's thought processing framework,' suggesting it's for preparing reasoning outputs for those systems. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., direct API calls or other reasoning engines) and does not specify prerequisites or exclusions.

    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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