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SammySnake-d

Fast Context MCP

by SammySnake-d
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Server Quality Checklist

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

  • Latest release: v1.2.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: one extracts an API key from a local installation, the other performs semantic code search. No overlap or ambiguity exists.

    Naming Consistency4/5

    Both tools use snake_case and follow a descriptive pattern (extract_* and fast_*). Although the verb style differs slightly (command vs. brand adjective), the naming is clear and consistent in format.

    Tool Count3/5

    With only two tools, the server feels minimal. While the tools are focused, the count is on the low end of what is typically expected, making it borderline appropriate.

    Completeness2/5

    The server covers API key extraction and semantic search, but lacks related operations like API key management, file content retrieval, or result navigation. Significant gaps exist for a context-focused server.

  • Average 4.5/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 11 of 11 community issues answered or closed in the last 6 months
    • 37 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • 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?

    Given no annotations, the description fully covers the tool's behavior: auto-detecting OS, reading a local database, and setting an environment variable. It could mention potential permissions or security considerations for reading a local database, but overall is transparent.

    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 concise with two clear, front-loaded sentences. No unnecessary information, every sentence adds value.

    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 no parameters, no output schema, and a straightforward operation, the description provides complete context: what the tool does, how it works (auto-detection), and the outcome (setting env var).

    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, so the baseline is 4. The description does not need to add parameter information, and it adequately explains that no parameters are needed due to auto-detection.

    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 the tool's purpose: extracting the Windsurf API key from the local installation, with OS auto-detection and env var setting. It distinguishes from the sibling tool 'fast_context_search' which serves a different function.

    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 explicitly states the outcome (setting the env var) but does not provide explicit guidance on when or when not to use this tool versus alternatives. Usage context is implied but not detailed.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    With no annotations provided, the description fully explains the search process: rounds of search-execute-feedback, auto fallback for tree depth if payload exceeds 250KB, and mentions API quota usage for more rounds. This provides comprehensive behavioral insight.

    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 a main overview and a parameter tuning guide. It is concise overall but the tuning guide could be slightly more compact. Still, it is clear and front-loaded with key purpose.

    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 6 parameters, 100% schema coverage, no output schema, and only one sibling, the description covers purpose, behavior, tuning, and response content (including [config] line). It provides sufficient context for an agent to use the tool effectively.

    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. The description adds valuable tuning context for tree_depth and max_turns beyond the schema, explaining payload/size errors and result completeness. For other parameters, the schema already suffices. Thus, a 4 is warranted.

    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 the tool performs AI-driven semantic code search, returns file paths with line ranges and grep keywords. It distinguishes itself from the only sibling, extract_windsurf_key, which extracts a 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?

    The description includes a parameter tuning guide that explains when to adjust tree_depth and max_turns based on errors or shallow results. It also advises using the [config] line in responses for retry decisions. However, it does not explicitly state when not to use this tool, but given only one dissimilar sibling, this is acceptable.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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