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martoc

MCP Spark Documentation Server

by martoc
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.2.17

  • Disambiguation5/5

    The two tools have clearly distinct purposes: search_documentation finds relevant pages based on a query, while read_documentation retrieves the content of a specific page. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: 'read_documentation' and 'search_documentation'. The naming is predictable and clear.

    Tool Count4/5

    With only two tools, the server is minimal but functional for its purpose of documentation access. While it could benefit from additional tools like listing sections or getting metadata, the current count is reasonable for a focused documentation server.

    Completeness4/5

    The server covers the essential operations for documentation browsing: searching and reading. However, it lacks features like listing available sections or navigating the documentation structure, which are minor but notable gaps.

  • 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
    • 7 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?

    With no annotations provided, the description carries full burden. It discloses that the tool reads full content and returns markdown or an error, implying a read-only operation. Adding an explicit read-only hint would improve transparency.

    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 Args and Returns sections, but it is slightly verbose for a simple one-parameter tool. Each sentence adds value, so it earns a high score.

    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 that an output schema exists, the description need not detail return structure. It covers the single parameter well and mentions the return type ('full markdown content'). Mentioning the sibling tool explicitly would improve completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Although the input schema has 0% description coverage, the description provides excellent context for the 'path' parameter with examples ('sql-ref/sql-syntax.md') and explains its origin from search results, fully compensating for the missing schema 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 the verb 'Read' and the resource 'specific Spark documentation page', and distinguishes itself from its sibling 'search_documentation' by mentioning that the path comes from search results.

    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 explains that the path should be a relative path to a documentation file and that it is returned by search results, providing clear context for when to use. However, it does not explicitly state when not to use or mention alternatives beyond the implied search tool.

    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 full-text search with stemming, optional section filtering, default and maximum limit, and the return format with fields. This provides sufficient behavioral context for an agent.

    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 very concise: a single line for purpose, then parameter descriptions in a clear format. No wasted sentences; every line adds value. The structure is easy to parse.

    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 the complexity (3 parameters, output schema present), the description covers all necessary aspects: input parameters with examples, output format, and behavioral details like stemming and default limits. It is complete for an agent to use correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema has 0% description coverage, so the description must compensate. It adds meaningful details: for 'query' it explains stemming, for 'section' it lists common values, for 'limit' it gives default and maximum. This fully compensates for missing schema 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 it searches Apache Spark documentation by keyword query. It specifies the resource (Apache Spark documentation) and verb (search), and the sibling tool 'read_documentation' suggests a complementary action, distinguishing this tool as the search interface.

    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 explains the tool's purpose but does not explicitly state when to use this tool versus alternatives like 'read_documentation'. It implies usage for searching documentation, but lacks guidance on when not to use it or when to prefer the sibling tool.

    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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