MCC Solutions

Get state-rule dataset records

get_dataset_records

Read-onlyIdempotent

Return records of one MCC dataset, optionally filtered to states, from official sources with check dates (CC BY 4.0). Without a key: a 3-record sample plus the plans. With an MCC Data API key (Bearer token): the full records, counted toward the plan's quota.

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": {
      "dataset": "<dataset>"
    },
    "name": "get_dataset_records"
  }
}

Input Schema

Name	Required	Description	Default
`states`	No	Two-letter codes, e.g. ["NJ","NY"]
`dataset`	Yes	Dataset id from list_datasets

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, closed-world, non-destructive behavior, so the description earns credit for going further: source provenance, check dates, CC BY 4.0 licensing, the 3-record sample degradation without a key, and quota consumption with a key. It stops short of describing pagination or response shape.

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?

Purpose comes first, then the two auth modes in parallel structure — no filler sentences. The parenthetical licensing note is slightly tangential but small and readable.

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?

For a two-parameter read tool with no output schema, the description covers what is returned (records with check dates), the filtering option, the auth requirement, and the sample-vs-full distinction. An agent has enough to call it correctly, with only return-format details left unspecified.

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?

Schema description coverage is 100%, with both parameters documented (two-letter state codes with an example, and dataset id pointing to list_datasets). The description adds no format or semantics beyond that, so baseline 3 applies.

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?

The description names a specific verb and resource ('Return records of one MCC dataset') and scopes it ('optionally filtered to states, from official sources with check dates'). It is clearly not a dataset-listing tool, though it never explicitly contrasts itself with list_datasets — that link only appears in the schema's parameter description.

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?

It gives concrete operating context: without a key you get a 3-record sample plus plans, with a Bearer token you get full records counted against quota. That tells an agent which mode it is in and what to expect, but there is no explicit when-not or alternative-tool routing.

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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