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Okareo MCP Server

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by okareo-ai

Save Scenario

save_scenario
Idempotent

Save a named scenario for quality tests. Provide dataset as JSONL content, file path, or rows. Existing scenario names return the current version.

Instructions

Save a named scenario for use in quality tests.

        Provide EXACTLY ONE dataset source: `content`, `file_path`, or
        `rows`.

        Prefer `file_path` for local .jsonl files — the server reads the
        file directly, so no rows pass through the assistant's context.
        When passing rows through the assistant instead, keep the dataset
        UNDER 2,000 rows (`content` with the file's text, or `rows` for
        tiny datasets). For 2,000 rows or more, always use `file_path` or
        upload directly to Okareo via the web app, SDK, or CLI, to avoid
        unnecessary token cost.

        If a scenario with the same name already exists, the existing
        scenario is returned (idempotent). Scenarios are immutable after
        creation — use create_scenario_version to create updated versions.

        Args:
            name: A unique name for the scenario.
            content: Raw JSONL text (one JSON object with 'input' and
                'result' per line). Only for datasets under 2,000 rows.
            file_path: Path to a local .jsonl file. Preferred — works for
                any size.
            rows: List of data rows, each with 'input' (any type) and
                'result' (any type). Use for small scenarios (< 20 rows).
            tags: Optional list of tags for categorizing the scenario.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
rowsNo
tagsNo
contentNo
file_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Confirms idempotent behavior (existing scenario returned) and adds that scenarios are immutable after creation. Also discloses that file_path avoids passing rows through assistant context and notes token cost considerations, going beyond the idempotentHint annotation.

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 structure is well-organized: opening purpose, usage guidance, idempotency note, and a clear Args list. Each sentence provides necessary information without unnecessary fluff, though slightly long due to parameter guidance.

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?

Covers parameter selection, constraints, idempotency, immutability, and alternatives. Given that an output schema exists, it needn't describe return values; it's complete for a tool of this complexity.

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?

With 0% schema coverage, the description provides detailed semantics for all 5 parameters, including constraints (e.g., 'one JSON object with input and result per line' for content, '< 20 rows' for rows) and trade-offs between sources.

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 'Save a named scenario for use in quality tests,' using a specific verb and resource. It distinguishes itself from sibling tools by explicitly noting that scenarios are immutable and that create_scenario_version is used for updates.

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?

Provides explicit guidance on providing EXACTLY ONE dataset source, with clear preferences (file_path for local files and large datasets, content/rows for small datasets under 2,000 rows). It also references alternative upload methods (web app, SDK, CLI) and points to create_scenario_version for updates.

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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