Skip to main content
Glama

search_documents

Find documents containing specific keywords or phrases. Returns ranked results with titles and content snippets for quick scanning.

Instructions

Search documents by keyword using full-text matching. Returns matching documents ranked by relevance with titles and content snippets. Use this when you need to find documents containing specific words or phrases. For semantic meaning-based search, use search_semantic instead. Demo: mock in-memory results only (no real document store). No auth in this sample; rate limit and timeout apply. On failure, returns an error or empty result set—no destructive operations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return. Use smaller values for quick lookups and larger values for comprehensive searches.
queryYesThe search query string. Supports keywords and phrases to match against document titles and content.
Behavior4/5

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

No annotations provided, but description covers key behaviors: full-text matching, mock results, no auth, rate limit/timeout, error handling, non-destructive. Lacks details on result sorting or case sensitivity.

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?

Description is efficiently structured: purpose, usage, alternative, caveats. No redundancy, each sentence adds value.

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 no output schema and 2 params, description adequately covers functionality, usage, and constraints. Minor gaps (e.g., search behavior details) but sufficient for agent to use correctly.

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 coverage is 100% with parameter descriptions. Description adds context about results (titles, snippets) but does not add meaning beyond what schema provides. Baseline 3 is appropriate.

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?

Description clearly states it searches documents by keyword using full-text matching, returns ranked results with titles and snippets. It distinguishes from search_semantic by specifying keyword vs semantic search.

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

Usage Guidelines5/5

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

Explicitly says when to use (find documents with specific words/phrases) and when not to (semantic search use search_semantic). Also provides caveats about demo, auth, rate limits, and non-destructive behavior.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/zhangpanda/gomcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server