Mostly Right

Get a dataset

get_dataset

Read-onlyIdempotent

One dataset's overview: title, summary, topics, publisher, publication and update dates, canonical page URL, star and table counts, and up to 50 tables with their id, title, license, immutable version_id, column count, column names and the capabilities the publisher enabled. Here and everywhere in this tool, publisher means the ACCOUNT that published the dataset — not the organisation the data was gathered from, which is a source's own publisher. Example: {"slug": "kden-metar-hourly"}. Column types, descriptions, units and published profiles are NOT here — call get_table_schema for one table when you need them. Tables follow curated order and may include display_title, description and section; main_table identifies the opening table. These labels do not change table identifiers. Use the returned table id (a UUID) with get_table_schema, sample_rows and query_table. Cite the dataset by canonical_url and the table by version_id.

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": {
      "slug": "<slug>"
    },
    "name": "get_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover read-only/idempotent/non-destructive, and the description still adds real behavior: the 50-table cap, curated ordering, the role of main_table, and the important guarantee that display_title/description/section labels do not alter table identifiers. It also says which field to cite (canonical_url for the dataset, version_id for the table).

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?

Front-loaded with the payload contents, then caveats, then the example and citation guidance — good ordering with no filler. The publisher-definition sentence is long and interrupts the flow, so it is dense rather than tight, but every clause still carries information an agent needs.

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?

There is no output schema, so the description carries the full burden of describing return shape and does so field by field, including the nested table objects and their quirks. Nothing essential for calling this tool correctly and interpreting the result is missing.

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

Parameters4/5

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

Schema coverage is 0% and the single parameter has no description in the schema, so the description must carry it. The worked example {"slug": "kden-metar-hourly"} conveys the expected shape, though it does not state the format rules (max length 120, slug vs id vs title) explicitly.

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?

"One dataset's overview" names the resource and then enumerates exactly what comes back (title, summary, topics, publisher, dates, URL, stars, table counts, up to 50 tables with id/license/version_id/column info). It is clearly distinguishable from get_table, get_table_schema and search_datasets by scope.

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?

Explicit exclusions and routing: column types/descriptions/units/profiles are NOT here and get_table_schema should be called instead, and the returned table id is stated to feed get_table_schema, sample_rows and query_table. The publisher/source-publisher distinction heads off a common misread before the agent calls.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Mostly Right tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?