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darbotlabs

Darbot Deepmind MCP Server

by darbotlabs
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TypeScript
Hybrid

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, there is no possibility of confusion or overlap between tools. The single tool 'darbot_deepmind' has a clearly defined and distinct purpose for structured problem-solving.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'darbot_deepmind' follows a consistent pattern with no deviations to evaluate.

    Tool Count2/5

    A single tool is too few for a server described as enabling 'dynamic and reflective problem-solving through structured thinking.' This suggests a complex domain that would benefit from multiple specialized tools (e.g., for different thinking modes or problem types), making the current count insufficient for the apparent scope.

    Completeness2/5

    The tool surface is severely incomplete for the domain. While the single tool covers a general problem-solving process, there are obvious gaps such as tools for specific analysis techniques, data handling, or integration with external resources that would be expected in a sophisticated problem-solving server.

  • Average 3.8/5 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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

  • 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 discloses key behavioral traits: the tool allows iterative adjustment of thoughts, supports revisions and branching, generates and verifies hypotheses, repeats until satisfied, and outputs a final answer. It covers process flow, flexibility, and output expectations, though it doesn't mention performance aspects like rate limits or error handling.

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

    Conciseness2/5

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

    The description is excessively long (over 400 words) with redundant sections. It repeats concepts (e.g., revision and branching are mentioned multiple times) and includes verbose lists that could be condensed. While structured with headings, it lacks front-loading of critical information and contains unnecessary elaboration, reducing efficiency for an AI agent.

    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 complexity (9 parameters, no output schema, no annotations), the description is quite complete: it explains the tool's purpose, usage guidelines, behavioral process, and parameter semantics in detail. It covers the iterative nature, hypothesis generation, and final output. However, it doesn't specify the format or content of the 'correct answer' output, leaving a minor 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 description coverage is 100%, so the baseline is 3. The description adds significant value with a 'Parameters explained' section that elaborates on each parameter's purpose and usage context (e.g., 'thought' can include revisions, questions, hypotheses; 'next_thought_needed' for adding thoughts even at the end). This provides semantic meaning beyond the schema's basic descriptions, compensating well for the high parameter count.

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

    Purpose3/5

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

    The description states the tool is for 'dynamic and reflective problem-solving through structured thinking' and 'helps analyze complex problems through an adaptive thinking process', which provides a general purpose. However, it lacks specificity about what concrete action the tool performs (e.g., does it execute analysis, generate reports, or simulate thinking?) and doesn't distinguish from siblings (though none exist). The description is vague about the actual output or mechanism beyond the thinking process.

    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 includes a clear 'When to use this tool' section with 7 specific scenarios (e.g., 'Breaking down complex problems into steps', 'Planning and design with room for revision'), providing explicit guidance on when this tool is appropriate. It also lists 11 'You should' instructions that further clarify usage, though no alternatives are mentioned (but no siblings exist).

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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