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Find a template

find_template
Read-only

Describe the matter in your own words; Dealroom answers with the contract types whose names or usual terms appear in the description (code, title, guide link), or "No template covers this. Such matters are usually handled by a lawyer." The matching is by words, with no AI model. A lawsuit or claim received, a subpoena, a data breach or a letter from a regulator is flagged as a time-sensitive matter outside the templates (outsideTemplates). It does not say what the user needs and names no lawyer. Free; no API key needed.

Input Schema

NameRequiredDescriptionDefault
langNoLanguage of titles, links and the messageen
queryYesThe matter in plain words, in English or Spanish

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior4/5

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

Annotations only declare readOnlyHint and openWorldHint; the description adds substantive behavior beyond that — matching is keyword-based with no AI model, the call is free with no API key, time-sensitive matters (lawsuits, subpoenas, data breaches, regulator letters) are flagged via outsideTemplates, and the return fields are named. This is real added context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is rambling and awkwardly structured, quoting the tool's own output messages verbatim ('code, title, guide link', 'No template covers this...') rather than front-loading the essential purpose. Sentences like 'It does not say what the user needs and names no lawyer' are confusing and read as describing a response payload rather than the tool.

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?

There is no output schema, so the description usefully names what comes back (contract types with code, title, guide link) and describes the no-match fallback and the time-sensitive flagging. It is fairly complete for a read-only lookup tool, though the tangled prose slightly reduces clarity.

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 both 'query' and 'lang'. The description reinforces that the query is a free-text matter in English or Spanish, but adds little syntax or constraint detail beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description does convey the core verb+resource: it matches a plain-language description of a matter against contract types by keyword. It is distinguishable from siblings like list_templates and get_template. However, the phrasing is roundabout, and sentences such as 'It does not say what the user needs and names no lawyer' have an ambiguous subject that muddies the purpose.

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

Usage Guidelines3/5

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

It implies usage ('Describe the matter in your own words') and reveals that a fallback path exists when nothing matches. But it never explicitly says when to choose this over list_templates, list_contract_types, or get_template, leaving the agent to infer the routing.

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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