dashai-mcp

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dashai_describe_dataset

Read-onlyIdempotent

Retrieve complete dataset details including schema, sample rows, and column types in a single call, eliminating the need to make multiple API requests.

Instructions

Returns everything needed to configure a training run over a dataset.

Gathers into a single call what the raw API splits into four (/{id}, /info, /types and /sample), because deciding which columns are input and which is output requires seeing them together.

Args: params (DescribeDataset): contains: - dataset_id (int): dataset id - include_sample (bool): include sample rows (default True)

Returns: str: JSON {"dataset": {...}, "info": {...}, "column_types": {...}, "sample": [...]} If one part is unavailable it comes back as null instead of failing whole.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "params": "<params>"
    },
    "name": "dashai_describe_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 17, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already mark this as read-only and idempotent, but the description goes further by disclosing that it merges four API calls and that missing parts are returned as null instead of failing the whole request. This adds meaningful behavioral context beyond the 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 purpose statement, rationale, Args, and Returns sections. It is slightly longer than necessary but each sentence serves a purpose, and the key information is front-loaded in the first sentence.

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 description covers the tool's purpose, why it exists, parameter semantics, return format, and partial-failure behavior. With supportive annotations and a clear output schema, it is fully sufficient for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides rich descriptions for both parameters (e.g., include_sample says 'Include ~10 sample rows. Set to false if the dataset has very wide columns.'). The description's Args section merely restates the schema without adding new meaning, so it does not elevate above the baseline.

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 'Returns everything needed to configure a training run over a dataset,' clearly stating the specific verb (returns) and resource (dataset). It also explains that it aggregates four raw API endpoints, which distinguishes it from sibling tools like dashai_list_datasets and dashai_train_model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description conveys when to use this tool: when deciding which columns are inputs and outputs for training requires seeing them together. It gives clear context but does not explicitly name alternatives or state when-not-to-use conditions, falling slightly short of a 5.

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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