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Okareo MCP Server

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by okareo-ai

Get Test Run Results

get_test_run_results
Read-onlyIdempotent

Retrieve test run results by ID or name, including aggregate metrics and per-row check scores. Optionally include full conversation transcripts and paginate large result sets.

Instructions

Load the results of a specific test run.

    Look up by test run ID (UUID) or by name (returns the most recent run
    matching that name). Returns aggregate metrics and per-row check scores.

    By default, conversation transcripts (model_input/model_result) are
    excluded to keep responses concise. Set include_transcripts=True to
    include full transcripts. Use get_conversation_transcript to inspect
    a single conversation's transcript without loading all of them.

    Supports pagination via limit and offset for large result sets.

    Args:
        test_run_id: The UUID of the test run. Takes precedence over name.
        name: The name of the test run. Returns the most recent match.
        include_transcripts: Include full model_input and model_result in
            each data point. Defaults to False (scores only). Set True for
            full conversation transcripts.
        limit: Maximum number of data points to return. 0 (default) returns
            all data points. Use with offset for pagination.
        offset: Number of data points to skip. Defaults to 0.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
limitNo
offsetNo
test_run_idNo
include_transcriptsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond annotations: transcripts are excluded by default for conciseness, test_run_id takes precedence over name, and name returns the most recent match. It does not contradict annotations.

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 a summary paragraph and an Args list. It is somewhat lengthy but every sentence adds useful detail. The front-loaded purpose and logical flow make it easy to scan. Slight redundancy exists between the prose and Args list, but it aids clarity.

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 tool has an output schema, so return values are covered. The description explains lookup methods, alternative tool for transcripts, pagination, and default behavior. It is complete for a read-only query tool with 5 parameters; no crucial information is missing.

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?

The schema has 0% description coverage, so the description carries full burden. The Args section provides detailed semantics for all 5 parameters: precedence of test_run_id, behavior of name, effect of include_transcripts, and pagination semantics of limit/offset. This far exceeds what the schema provides.

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 the tool loads results of a specific test run, with specific lookup methods (by UUID or name) and what it returns (aggregate metrics and per-row check scores). It distinguishes itself from sibling tools like get_conversation_transcript and list_test_runs.

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 explicitly tells when to use this tool vs alternatives: 'Use get_conversation_transcript to inspect a single conversation's transcript without loading all of them.' It also explains when to use include_transcripts and pagination, giving clear contextual 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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