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Get dataset details

get_dataset
Read-onlyIdempotent

Full record for one dataset: fields with descriptions, record and state counts, coverage measured on the rows (a list in 14 states says "partial U.S.: 14 states"; coverageDetail lists the states, rows per state and the states with none), whether it can be searched by distance, advertised refresh cadence AND the real last-modified date of the file, FAQs, sample URL and the dataset's page on locationlists.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesDataset slug from search_datasets, e.g. bobcat-dealers

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint=false, so safety is covered. The description goes beyond that by explaining interpretive behavior an agent could not otherwise know: coverage is measured on rows ("partial U.S.: 14 states"), coverageDetail breaks down rows per state, and advertised refresh cadence may differ from the real last-modified date. That semantic context is genuinely additive.

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?

A single dense sentence, but it is front-loaded with the core purpose ("Full record for one dataset") before the enumeration of fields. The parenthetical coverage example earns its place by clarifying a non-obvious metric; nothing is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 return-value burden and does so thoroughly, naming fields, counts, coverage detail, distance-searchability, refresh cadence, FAQs and URLs. What is missing is the usage framing (why this vs. searching or buying), the one gap that keeps it from being fully complete.

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?

Only one parameter, and schema description coverage is 100%, so the baseline is 4. The description adds nothing about slug handling, but with a single fully documented parameter there is nothing meaningful left to compensate for.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ("Full record for one dataset") and then enumerates the exact contents of that record, which is far more specific than a generic "get" description. It does not name or contrast with siblings like search_datasets or buy_dataset, so an agent must infer the relationship.

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

Usage Guidelines2/5

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

No when-to-use or when-not-to-use guidance appears in the description. The only routing hint ("slug from search_datasets") lives in the schema's parameter description, not the tool description, so the agent gets no explicit statement that this is the detail-lookup follow-up to a search.

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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