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Simonsms

TiDB RAG MCP Server

by Simonsms

Search Knowledge

tidb_search_knowledge
Read-onlyIdempotent

Search knowledge entries by keyword in the TiDB RAG knowledge base. Filter by category, control result limits, and paginate to locate relevant information.

Instructions

Search knowledge entries by keyword in TiDB RAG knowledge base.

This tool searches for knowledge entries matching a query string in title or content.

Args:

  • query (string): Search query string (1-500 characters)

  • limit (number): Maximum results to return, 1-100 (default: 20)

  • offset (number): Number of results to skip for pagination (default: 0)

  • category (string, optional): Filter by category

  • response_format ('markdown' | 'json'): Output format (default: 'markdown')

Returns: For JSON format: { "total": number, // Total matching entries "count": number, // Entries in this response "offset": number, // Current pagination offset "items": [...], // Array of matching knowledge entries "has_more": boolean, // Whether more results exist "next_offset": number // Offset for next page (if has_more) }

Examples:

  • Simple search: { "query": "MCP" }

  • Search with category filter: { "query": "向量", "category": "数据库" }

  • Paginated search: { "query": "RAG", "limit": 10, "offset": 10 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return (1-100)
queryYesSearch query string
offsetNoNumber of results to skip for pagination
categoryNoFilter by category
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior4/5

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

The annotations already cover safety traits (readOnlyHint, idempotentHint, non-destructive), so the description's added value is appropriate: it explains that results match title/content, documents the markdown/json response formats, and details pagination semantics like has_more and next_offset. This goes beyond the structured annotations without contradicting them.

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 organized with clear sections: purpose, args, returns, and examples. It is longer than minimal, but the extended return-shape documentation is justified because there is no output schema. The only minor redundancy is the second sentence partially restating the first.

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 that there is no output schema, the detailed JSON return structure and pagination fields are essential and are fully provided. Examples cover simple search, category filtering, and pagination. The definition is complete enough for an agent to invoke the tool correctly; cross-tool routing is absent but is already scored separately under usage guidelines.

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 baseline is 3. The Args section largely restates the schema: query length, limit range, default values, category filtering, and response_format enum. The examples add usage context but not new parameter-level meaning, so the description neither hurts nor significantly supplements the schema.

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 opening sentence names a specific verb and resource: 'Search knowledge entries by keyword in TiDB RAG knowledge base.' It further clarifies that matching occurs in title or content, which conveys a lexical-scope distinction from siblings. However, it never explicitly names or contrasts sibling tools such as tidb_vector_search, so differentiation is implicit rather than stated.

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 explicit when-to-use or when-not-to-use guidance. The description does not mention alternatives like tidb_list_knowledge for browsing, tidb_get_knowledge for single entries, or tidb_vector_search for semantic search. The keyword-scoped phrasing implies one usage context, but no exclusions or routing rules are provided.

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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