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uarlouski

TestRail MCP Server

get_cases

Read-onlyIdempotent

Retrieve test cases for a project with filters by section, priority, type, or custom fields. Returns case IDs, titles, and any requested fields.

Instructions

Get all test cases for a project. Filter by section, API params (priority, type), or any field including custom fields via 'where'. Returns case IDs, titles, and any additional requested fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesThe ID of the project. Use get_projects to find available projects
sectionNoSection filter configuration. Use get_sections to find available sections
filterNoOptional API-side filters (more efficient for large datasets). Supported: priority_id, type_id, created_by, updated_by, milestone_id, refs, created_after, created_before, updated_after, updated_before. Use comma-separated values for IDs. Example: {"priority_id": "1,2", "type_id": "3"}
whereNoOptional client-side filter for any field including custom fields (filters after fetching all cases). Supports exact value matching. Example: {"custom_automation_status": 1, "priority_id": 2}
fieldsNoAdditional fields to include in response beyond id, title, and suite_id. Use get_case_fields to see available fields. Example: ["priority_id", "type_id", "custom_automation_status"]
output_fileNoAbsolute file path to save the JSON response to. Use this for large datasets to avoid blowing up context limits.
Behavior4/5

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

Annotations already declare readOnlyHint=true; description adds value by noting the return of case IDs/titles and the output_file parameter for large datasets to avoid context limits. No contradictions.

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 two sentences, front-loads the main purpose, and provides all necessary information without unnecessary words.

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?

The description covers the main features and output, but does not explicitly mention pagination behavior for large result sets beyond the output_file hint. Slight gap given no output schema.

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 description adds examples for filter/where/fields parameters and explains the purpose of output_file, enhancing 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 clearly states 'Get all test cases for a project' with specific filtering options and return fields. It distinguishes from siblings like get_case (singular) and get_case_fields (fields only).

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

Usage Guidelines2/5

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

The description lacks explicit guidance on when to use this tool over siblings like get_case or update_case. While the purpose is clear, no comparisons or exclusions are provided.

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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