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mshegolev

allure-testops-mcp

allure_get_project_statistics

Read-onlyIdempotent

Get summary statistics for an Allure project, including test case count, automation rate, and the last closed launch's pass/fail breakdown.

Instructions

Get summary statistics for an Allure project.

Returns TC count, automation rate, and the last closed launch's pass/fail breakdown. Performs 3-4 API calls — progress is reported via MCP Context (visible as progress updates in compatible clients).

Args: project_id: Allure project ID (see allure_list_projects). ctx: MCP Context injected by FastMCP (used for progress reporting; never supplied by the agent directly).

Returns: dict with keys: - project_id (int) - total_test_cases (int) - automated_test_cases (int) - manual_test_cases (int) - automation_rate_pct (float) - last_launch_id (int | None): latest closed launch - last_launch_name (str | None) - last_launch_passed / last_launch_failed / last_launch_broken (int) - last_launch_total (int) - recent_launches_count (int): launches examined to find the latest closed one

Examples: - "How automated is project 63?" -> project_id=63, read automation_rate_pct - "What was the last passing run for project 175?" -> read last_launch_passed

Don't use when:
- You need per-test detail (use ``allure_get_test_results``).
- You need the full launch history (use ``allure_list_launches``).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesAllure project ID (discover via allure_list_projects).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
total_test_casesYes
automated_test_casesYes
manual_test_casesYes
automation_rate_pctYes
last_launch_idYes
last_launch_nameYes
last_launch_passedYes
last_launch_failedYes
last_launch_brokenYes
last_launch_totalYes
recent_launches_countYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that the tool performs 3-4 API calls with progress reported via MCP Context, which is useful behavioral insight beyond the annotations. 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.

Conciseness4/5

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

The description is well-structured with clear sections (summary, Args, Returns, Examples, Don't use). It is front-loaded with the purpose. While somewhat verbose, every section adds value and is easy to parse.

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?

The description fully compensates for the lack of an output schema by providing a detailed return dict specification. It includes examples and instructions on interpreting results. For a simple one-param tool, it is exceptionally complete.

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?

The sole parameter (project_id) is well described in the schema and the description adds examples of usage. With 100% schema coverage, the baseline is 3, and the description adds marginal value but not significant new meaning.

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 summary statistics for an Allure project' and lists specific outputs (TC count, automation rate, last launch breakdown). It distinguishes from siblings by noting when not to use it (for per-test detail or full launch history, referencing sibling tools).

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 provides explicit 'Don't use when' instructions with alternative tools (allure_get_test_results, allure_list_launches) and includes concrete examples for typical queries. This gives clear guidance on appropriate vs. inappropriate usage.

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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