Skip to main content
Glama

search

Search captured LLM calls by prompts, outputs, system prompts, agent names, or user IDs to find specific interactions related to a topic, error, or agent.

Instructions

Full-text search across all captured LLM calls. Searches prompts, outputs, system prompts, agent names, and user IDs. Use this to find calls related to a topic, error, or agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return (default 20, max 100).
queryYesSearch term to look for across all captured calls.
Behavior3/5

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

No annotations present, so description carries full burden. It explains search scope and purpose but omits details like case sensitivity, pagination behavior, error handling on empty query, or performance implications.

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?

Two concise sentences that front-load the main action and purpose. Every sentence adds value, no redundancy or fluff.

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 annotations or output schema and only two simple params, the description adequately explains what the tool does and when to use it. Could mention that it searches only captured calls and maybe result format, but it's sufficient.

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 covers both parameters (q and limit) with descriptions. The tool description does not add extra meaning beyond what the schema provides, meeting baseline for 100% coverage.

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's a full-text search over all captured LLM calls, enumerates fields searched (prompts, outputs, system prompts, agent names, user IDs), and distinguishes from sibling tools (list/explain/get) by being the only search tool.

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?

Explicitly says 'Use this to find calls related to a topic, error, or agent,' providing clear use context. However, no when-not-to-use or alternative tool references, which would strengthen guidance.

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/ShekharBhardwaj/AgenticLedger'

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