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okareo-ai

Okareo MCP Server

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by okareo-ai

List Simulations

list_simulations
Read-onlyIdempotent

Retrieve a list of past simulation runs with names, IDs, timestamps, and status. Filter by target or scenario, and choose summary or detailed metrics.

Instructions

List past simulation runs in the project.

    Returns simulation run names, IDs, timestamps, and status, sorted by
    most recent first. Defaults to the 10 most recent runs in summary mode.

    Use detail_level="detailed" to include model_metrics and additional
    fields (limit is capped to 5 in detailed mode to prevent overflow).

    Use get_test_run_results with the returned test_run_id to retrieve
    per-row scores (transcripts excluded by default). Then use
    get_conversation_transcript with a scenario_index to inspect
    individual conversation transcripts.

    Args:
        target_name: Optional filter — only show simulation runs using
            this target.
        scenario_name: Optional filter — only show simulation 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.
        detail_level: "summary" (default) returns compact results without
            model_metrics. "detailed" returns full results with metrics
            (limit capped to 5).
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
target_nameNo
detail_levelNosummary
scenario_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description adds behavioral details beyond annotations, including sorting order ('sorted by most recent first'), default limit of 10, the cap at 5 for detailed mode, and the distinction between summary and detailed outputs. These are not conveyed by the readOnly/idempotent annotations, making the description valuable.

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 well-structured with a clear summary, a usage workflow paragraph, and a labeled Args section. It is relatively detailed but each sentence serves a purpose, and the use of a code block for parameters improves readability.

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?

Given the presence of an output schema (which covers return structure), the description covers the tool's purpose, all parameters, behavioral constraints, and its place in the workflow with sibling tools. It is complete enough for an agent to select and invoke the tool correctly without additional information.

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 input schema has no parameter descriptions (0% coverage), but the description thoroughly explains each parameter: target_name and scenario_name as optional filters, limit with its default and 0-to-return-all behavior, and detail_level with its two allowed values and the 5-cap implication. This fully compensates for the schema's lack of documentation.

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's function with a specific verb and resource ('List past simulation runs in the project'), and details what is returned (names, IDs, timestamps, status, sorted most recent first). It differentiates from sibling tools like get_test_run_results and get_conversation_transcript by outlining the intended workflow.

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?

It provides explicit guidance on when to use this tool versus downstream tools, instructing to use get_test_run_results with the returned test_run_id and get_conversation_transcript with scenario_index. It also explains when to switch detail_level based on whether model_metrics are needed. This contextualizes usage within the broader API workflow.

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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