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Okareo MCP Server

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by okareo-ai

List Test Runs

list_test_runs
Read-onlyIdempotent

Retrieve and filter past test runs, showing names, IDs, timestamps, status, and summary scores for evaluation history.

Instructions

List past test runs in the project.

    Returns test run names, IDs, timestamps, status, and summary scores,
    sorted by most recent first. Defaults to the 10 most recent runs.
    Optionally filter by model name, scenario name, or type.

    For simulation runs (type MULTI_TURN), use get_test_run_results with the
    returned test_run_id to retrieve full conversation transcripts and per-turn
    check scores.

    Args:
        model_name: Optional filter — only show test runs using this model.
        scenario_name: Optional filter — only show test runs using this scenario.
        limit: Maximum number of runs to return, sorted by most recent first.
            Defaults to 10. Set to 0 to return all runs.
        simulation_only: When True, return only MULTI_TURN simulation runs.
            Useful for browsing past simulation results without NL_GENERATION or
            other test run types appearing in the list.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
model_nameNo
scenario_nameNo
simulation_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive; description adds default limit=10, limit=0 returns all, and that results are sorted by most recent. Also clarifies that simulation_only filters to MULTI_TURN type.

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 organized with a clear opening sentence, followed by return values, defaults, filters, and a pointer to an alternative tool. The Args section is structured and each line adds value. No wordiness.

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 a rich output schema present, the description need not detail return types, but it already summarizes return fields. It covers filters, defaults, and the recommended next step for transcripts. Sufficient for an agent to use correctly.

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 has zero field descriptions, but the description documents every parameter (model_name, scenario_name, limit, simulation_only) with defaults and purpose, fully compensating. The simulation_only explanation gives extra context on what MULTI_TURN simulation runs are.

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 it lists past test runs, specifies returned fields (names, IDs, timestamps, status, summary scores) and sort order. Clearly differentiates from get_test_run_results by directing users there for transcripts.

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 directs users to get_test_run_results for simulation run details, implying this tool is for summary listing. Explains filtering options with examples of when simulation_only is useful.

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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