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Okareo MCP Server

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by okareo-ai

Run Test

run_test

Submit a quality test to evaluate a model's performance against a scenario using specified checks, and get a test run ID to retrieve scores later.

Instructions

Submit a quality test that evaluates a model against a scenario using checks.

    Returns promptly so the call never times out on long runs. Short runs return
    ``status: "finished"`` with results ready; longer runs return
    ``status: "running"`` with the ``test_run_id`` and ``app_link`` — the run
    continues to completion on its own. In both cases, poll get_test_run_results
    with the returned test_run_id to retrieve scores.

    Args:
        scenario_name: Name of the scenario to evaluate against.
        model_name: Name of the registered model to evaluate.
        checks: List of check names to apply (e.g., ["coherence", "relevance"]).
            Use list_checks to discover available checks and pick from the
            category matching the task and modality — do not use
            voice-specific checks for text evaluations (or vice versa);
            checks outside voice-specific categories suit both.
        name: Optional human-readable name for this test run.
        type: Type of evaluation. Defaults to NL_GENERATION. Valid values:
            NL_GENERATION, INFORMATION_RETRIEVAL, MULTI_CLASS_CLASSIFICATION,
            INVARIANT, MULTI_TURN, AGENT_EVAL.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
typeNoNL_GENERATION
checksYes
model_nameYes
scenario_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations already mark this as non-read-only and non-idempotent, but the description adds essential async behavior: it returns promptly, may return status 'finished' or 'running' with test_run_id and app_link, and continues on its own. This fully discloses the lifecycle and interaction pattern.

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 compact but dense, with no fluff. The Args block is a simple list within a paragraph, which could be more readable as a bulleted list, but every sentence earns its place and it avoids redundancy with the schema.

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?

For a 5-parameter tool with an output schema present, the description covers all dimensions: what it does, how to use it, async behavior, parameter semantics, and interaction with sibling tools. The output schema handles return value details, so no additional return documentation is needed.

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 parameter descriptions are absent (0% coverage), so the description carries full responsibility. It defines every parameter: scenario_name, model_name, checks with an example and guidance on selection, name as optional, and type with all valid enum values. This compensates completely for the schema gap.

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 opens with a specific verb+resource: 'Submit a quality test that evaluates a model against a scenario using checks.' This clearly distinguishes run_test from siblings like run_simulation or get_test_run_results.

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 explains when to use this tool for submitting a test, notes that short runs finish immediately while long runs return a status and ID, and explicitly directs polling via get_test_run_results. It also advises using list_checks and warns against mismatched check categories, providing actionable when-to-use guidance.

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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