Skip to main content
Glama

search

Read-only

Find projects, boards, and items across your organization with a text query. Get matching results with IDs and links, then use an ID to retrieve the complete record.

Instructions

Full-text search across the projects, boards and items of the organization. Returns { results: [{ id, title, url }] } — the shape ChatGPT connectors and deep research expect; pass a result id to fetch for the full record. When you already know the board, query_items is cheaper and complete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results, default 20
queryYesSearch text
projectIdNoLimit the search to one project

Schema Changelog

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

  1. First observedv0.7.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds valuable behavioral context beyond that: it returns lightweight id/title/url results, not full records, and requires a follow-up fetch for complete data. It also reveals that search is scoped across projects, boards, and items rather than a single board.

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 with no wasted words. The first sentence states what the tool does and its scope; the second explains the return shape and next step; the third gives a direct comparison to a sibling tool. Every sentence earns its place and the most important information is front-loaded.

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?

For a read-only search tool with a small, well-documented schema, the description covers everything needed: scope, output shape, follow-up action, and the alternative tool to use when appropriate. Since it includes the return shape and routing guidance, the lack of an output schema is not a gap. The tool is fully callable from the information provided.

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 schema already documents all three parameters: query text, max limit/default, and optional projectId filter. The description adds no new parameter-level detail beyond the overall full-text search scope. This matches the baseline of 3 where the schema carries the heavy lifting.

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?

The description opens with a specific verb and resource: 'Full-text search across the projects, boards and items of the organization.' It also clearly distinguishes itself from query_items by noting when that sibling is more appropriate. The return shape is stated explicitly, removing ambiguity about what the tool produces.

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?

The description explicitly tells the agent when to prefer query_items instead: 'When you already know the board, query_items is cheaper and complete.' It also instructs the agent to 'pass a result id to fetch for the full record,' routing to the fetch tool appropriately. This gives clear decision criteria rather than leaving usage to inference.

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

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/shimon-ks/tasklite-mcp'

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