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Search this workspace's jobs, customers, engineers and projects, in every brand the user holds, by free text, for research and knowledge features that expect a search-and-fetch connector. Returns up to 20 documents with ids to pass to fetch. In a conversation prefer the task tools: find_customer, find_resource, find_project and get_job answer more precisely and carry the guidance you need.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree text matched against job titles and numbers, customer names, emails and postcodes, engineer names, and project names.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

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 and openWorldHint=false, and the description supplements them with non-obvious behavior: cross-brand reach ('every brand the user holds') and a hard result cap of 20 documents with ids to hand to fetch. It stops short of describing ranking, matching or failure behavior, so it is strong but not exhaustive.

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 sentences, both load-bearing: the first defines scope and the fetch handoff, the second redirects to the more precise task tools. Scope and constraints are front-loaded with no filler or restatement of the title.

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?

With an output schema present and annotations covering the safety profile, the description only needs to convey scope, limits and routing — all of which it does. Nothing required to select or invoke this tool correctly is missing.

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% and the single parameter's own description already enumerates the matched fields (job titles/numbers, customer names, emails, postcodes, engineer names, project names) in more detail than the tool description does. The phrase 'by free text' adds little beyond the schema, so baseline 3 applies.

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?

States a specific verb (search) plus the exact resource scope: jobs, customers, engineers and projects, across every brand the user holds, by free text. It also identifies the audience ('research and knowledge features that expect a search-and-fetch connector'), which separates it from the task-oriented siblings at a glance.

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 routes the agent: 'In a conversation prefer the task tools: find_customer, find_resource, find_project and get_job answer more precisely and carry the guidance you need.' That names the alternatives and the condition that selects them, which is the strongest form of when/when-not guidance.

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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