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MCP Spark Documentation Server

MCP Spark Documentation Server

by martoc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

search_documentation

Search Apache Spark documentation by keyword to locate relevant pages; filter by section or cap results as needed.

Instructions

Search Apache Spark documentation by keyword query.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of results to return (default: 10, max: 50).
queryYesSearch terms to find in the documentation. Supports full-text search with stemming (e.g., "stream" matches "streaming", "streams").
sectionNoOptional section to filter results. Common sections include: 'sql-ref', 'api', 'streaming', 'mllib', 'graphx', 'structured-streaming', etc.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.3.7
    • addedInput schema / properties / limit / description
      Added value: +"Maximum number of results to return (default: 10, max: 50)."
    • addedInput schema / properties / query / description
      Added value: +"Search terms to find in the documentation. Supports\n   full-text search with stemming (e.g., \"stream\" matches\n   \"streaming\", \"streams\")."
    • addedInput schema / properties / section / description
      Added value: +"Optional section to filter results. Common sections include:\n     'sql-ref', 'api', 'streaming', 'mllib', 'graphx',\n     'structured-streaming', etc."
  2. Changed1 schema field changedv0.2.6
    • addedInput schema / additionalProperties
      Added value: +false
  3. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations supplied, the description carries the full behavioral burden and discloses almost nothing: no ranking behavior, result shape, pagination, or scope limits. The only behavioral signal is 'keyword query', which is thin for a tool with no annotation coverage.

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?

A single front-loaded sentence with no filler, so it is efficient and easy to scan. It is arguably too terse for the tool's role, but that gap is captured under other dimensions rather than as verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple: three fully documented parameters and an output schema, so return values need no explanation here. The remaining gap is routing guidance against read_documentation and any note on search behavior, which the description omits.

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

Parameters3/5

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

Schema description coverage is 100%, so the query, limit, and section parameters are already fully documented, including stemming behavior and common section values. The description adds no parameter meaning beyond that, which meets the baseline for fully covered schemas.

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 gives a specific verb and resource ('Search Apache Spark documentation by keyword query'), so an agent knows exactly what the tool does. It does not differentiate from the sibling read_documentation, leaving the search-vs-read choice to the name alone.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus read_documentation, nor any prerequisite or exclusion. The only hint is the phrase 'keyword query', which implies lookup-by-term usage but is never made explicit.

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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