Skip to main content
Glama

search_semantic

Find documents by meaning similarity instead of exact keywords. Use when user intent matters more than exact wording.

Instructions

Search documents using semantic embedding-based matching. Returns results ranked by meaning similarity rather than exact keyword match. Use this when the user's intent matters more than exact wording. For exact keyword matching, use search_documents instead. Demo: simplified mock (no real embeddings API); no auth; same rate limit/timeout as other tools. Read-only; does not modify documents.

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?

Discloses demo nature, no auth, same rate limits, read-only behavior. Without annotations, it covers key behavioral aspects but could detail output format or ordering (e.g., threshold).

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?

Three sentences, each carrying essential information: purpose, usage guidance, and behavioral transparency. No redundant or irrelevant content.

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?

Covers purpose, usage, behavioral aspects for a simple 2-param tool. Missing explicit statement about output format or result structure, but adequate given no output schema.

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 baseline is 3. The description adds context that query drives semantic matching but does not add new parameter semantics beyond what schema provides.

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?

Clearly states the tool's purpose with specific verb and resource ('Search documents using semantic embedding-based matching') and distinguishes from sibling tool 'search_documents' which does exact keyword matching.

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 instructs when to use ('user's intent matters more than exact wording') and when not to ('For exact keyword matching, use search_documents instead'), providing clear alternative.

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