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TripQi

Fast Context MCP

by TripQi
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JavaScript
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools serve completely distinct purposes: one performs AI-powered code search, and the other extracts an API key. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tools use snake_case and follow a descriptive noun-verb pattern (fast_context_search, extract_windsurf_key). The naming is mostly consistent, though one is more of an adjective_noun_verb while the other is a clear verb_noun.

    Tool Count3/5

    With only two tools, the server feels thin for its purpose. However, it is narrowly focused on AI-driven search, and each tool is substantial, so the count is borderline acceptable.

    Completeness4/5

    The core search workflow is covered by the search tool, which includes configurable parameters for depth and turns. The key extraction is a necessary setup step. There are no obvious dead ends, though a few extra utilities (e.g., search history or config management) could enhance completeness.

  • Average 4.4/5 across 2 of 2 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

  • Behavior4/5

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

    No annotations are provided, so the description carries the burden. It discloses OS auto-detection, reading from the local database, and setting the result as an env var. It does not mention error cases or prerequisites, but it is transparent about its main side effect.

    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 two sentences, front-loaded with the primary action, and every sentence adds value without redundancy.

    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 zero-parameter tool with no output schema, the description covers the core function and side effect. It lacks mention of failure cases or requirements, but is otherwise complete for its simplicity.

    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 zero parameters and 100% schema coverage (empty), so the baseline is 4. No additional parameter explanation is needed.

    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 'Extract Windsurf API Key from local installation', a specific verb+resource with source. It distinguishes itself from the unrelated sibling fast_context_search.

    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 provides clear context for when to use the tool (to get the API key and set it as an env var), and the sibling tool is unrelated, so no exclusions are needed. It does not explicitly list alternatives, but usage is apparent.

    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 full burden. It discloses the multi-round search-execute-feedback mechanism and the '[config] line showing actual parameters used' for retry adjustments. It does not cover error handling, but it adds meaningful behavioral context beyond the schema.

    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?

    Well-structured with a clear purpose statement, a bulleted parameter tuning guide, and a closing note about the config line. Every sentence earns its place, and the front-loaded purpose aids quick comprehension.

    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?

    With 5 parameters, no output schema, and no annotations, the description covers purpose, tuning, and response characteristics effectively. It could be improved by explicitly stating output format or error behavior, but the content is sufficient for an agent to use the tool correctly.

    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%, so baseline is 3. The description adds a valuable tuning guide with diagnostic heuristics (e.g., 'If you get a payload/size error, REDUCE this value' for tree_depth) and explains the functional meaning of max_turns rounds, going beyond the schema field descriptions.

    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 'AI-driven semantic code search' with specific outputs: 'relevant file paths with line ranges, plus suggested grep keywords'. This specific verb+resource combination distinguishes it from the unrelated sibling tool extract_windsurf_key.

    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?

    Provides actionable tuning guidance (e.g., 'If search results are too shallow... INCREASE this value') and suggests using max_turns=1 for 'quick rough answer'. However, it does not explicitly mention when not to use or alternative tools, though the sibling is unrelated and the context implies use cases.

    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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  • Evaluate tool definition quality.

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