Mostly Right

Search public datasets

search_datasets

Read-onlyIdempotent

Ranked search over every public dataset on Mostly Right: titles, summaries, readmes, table titles and column names, most relevant first. No account needed. Example: {"query": "hourly airport weather observations", "limit": 10}. Returns {datasets: [{id, slug, title, summary, topics, publisher, published_at, canonical_url, snippet, matching_table, why}], next_cursor}; snippet, matching_table ({id, title}, the table that matched best, usable with get_table_schema) and why (one line on what matched) are present when you sent a query. publisher is the ACCOUNT that published the dataset here, never the organisation that publishes the data it was built from — those are named on the dataset page as its sources. Pass next_cursor back as cursor for the next page; a null next_cursor means there are no more. Omit query to list the most recently published datasets. Follow up with get_dataset(slug) for tables and schemas.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

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

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	No
`topic`	No
`cursor`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 9, 2026
- changedInput schema / properties / cursor / maxLength
```
Previous value: -300New value: +4096
```
First observedSep 10, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds real context on top: no authentication required, pagination via next_cursor with an explicit null terminator, per-field return semantics, and a caveat that `publisher` is the publishing account rather than the data's original organisation. That last point prevents a genuine misinterpretation an agent could not get from structured fields.

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 purpose and ranking, then call/return details, then follow-up — the ordering matches how an agent consumes it. It is a dense block of long sentences, and the multi-clause publisher clarification is heavy for one line, but nearly every clause carries operational value.

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, yet the description enumerates the return object, which fields appear conditionally, what `matching_table` is usable with, and how pagination terminates. Combined with the follow-up hint, an agent has everything needed to call and consume results correctly.

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%, so the description carries the burden. It explains `query` (with an inline example including `limit: 10`), and describes `cursor` precisely via its next_cursor round-trip. `topic` is never mentioned and `limit`'s bounds (1–100) are only implied by example, leaving one parameter undocumented.

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?

States a specific verb and resource ('Ranked search over every public dataset') plus the exact fields searched (titles, summaries, readmes, table titles, column names) and the ranking order. An agent can distinguish this public-catalog search from siblings like search_table or catalog_search without opening a schema.

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?

Gives concrete when-to-use context (public datasets, no account needed), an explicit no-query behavior ('Omit `query` to list the most recently published datasets'), pagination instructions, and a follow-up path ('get_dataset(slug)'). It stops short of naming an alternative for private/connected data, so the choice versus siblings remains implicit.

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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