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vilaabo

zephyr-scale-mcp

by vilaabo

search_test_plans

Read-only

Search test plans in Zephyr Scale using TQL queries. Filter by project, status, folder, and more with paginated results.

Instructions

Search Zephyr Scale test plans with a TQL query (GET /testplan/search). Returns { startAt, maxResults, count, isLast, values }; isLast is the heuristic count < maxResults. Paginate with startAt (default 0) and maxResults (default 50; the API server-side default is 200). TQL syntax is strict: spaces around operators are mandatory, string values go in double quotes, and the only logical connector is AND (no OR). Commonly supported test plan fields are projectKey, folder, name and status (e.g. projectKey = "PROJ" AND status = "Approved") — the exact set varies by Zephyr Scale version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesTQL query, e.g. projectKey = "PROJ" AND folder = "/Releases"
fieldsNoReturn only these fields, e.g. ["key","name","status"]; sent to the API as a comma-separated list
startAtNo0-based index of the first result to return (default 0)
maxResultsNoMaximum number of results to return (default 50; the API server-side default is 200)
Behavior5/5

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

The description extensively discloses behavioral traits: return structure (`startAt`, `maxResults`, `count`, `isLast`), the `isLast` heuristic, pagination defaults, server-side default for `maxResults`, and strict TQL syntax rules. This goes far beyond the `readOnlyHint` annotation.

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?

The description is a single paragraph with multiple sentences, each providing essential information. It is front-loaded with purpose and contains no redundant or irrelevant details. Every sentence earns its place.

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?

Despite having no output schema, the description fully explains the return structure and pagination behavior. It covers all necessary aspects for correct usage, including TQL syntax and common fields, making it complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds substantial value: TQL syntax examples for the `query` parameter, default values for `startAt` and `maxResults`, explanation of server-side default, and behavior of `fields` as comma-separated list. This significantly enhances understanding beyond the schema.

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 starts with 'Search Zephyr Scale test plans with a TQL query (GET /testplan/search)', clearly stating the verb, resource, and method. It distinguishes this tool from siblings like `search_test_cases` and `search_test_runs` by specifying test plans.

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?

The description provides detailed usage guidance on TQL syntax, pagination parameters, and common fields. While it doesn't explicitly state when to use this tool versus alternatives, the context and sibling names imply it is for test plans specifically.

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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