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USDA NASS MCP Server

USDA NASS MCP Server

by kin5
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_full_dataset

Retrieve all available USDA NASS agricultural statistics for a specified commodity, location, and time using a structured query.

Instructions

Get all available data for a given commodity, location, and time.

Args:
    query: Dictionary with three keys, "commodity", "location", and "time", each with a dictionary of parameter and value pairs.

Returns:
    A string containing the API response data or an error message.

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": {
      "query": "<query>"
    },
    "name": "get_full_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full burden. It discloses only that failures come back as an error string; it says nothing about authentication, rate limits, whether the result is paginated or truncated, or how large a 'full dataset' response can be — a real risk given the tool's name.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is front-loaded and clear, but the docstring-style 'Args' and 'Returns' blocks are largely boilerplate that duplicates the schema and the existing output schema. It is short enough not to be a burden, but the space is not well spent.

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

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with a deeply nested filter schema and no annotations, the description leaves too much unexplained: filter semantics, expected value formats, response size behavior, and error conditions are all missing. The output schema covers return shape, but nothing covers how to construct a valid, non-explosive query.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, and it only restates the top-level shape (three keys, each a dict of parameter/value pairs) that the schema already encodes. It never explains which of the many location or commodity fields actually filter, how multiple fields combine, what value formats are expected, or which of the three time variants to choose.

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 and resource ('Get all available data') scoped by commodity, location, and time, which is enough to distinguish it from get_db_record_count and get_param_values in practice. It stops short of naming those siblings explicitly, so it lands at 4 rather than 5.

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?

There is no when-to-use guidance: nothing says whether to reach for this instead of get_param_values when exploring, or get_db_record_count when sizing a query. The phrase 'all available data' faintly implies broad retrieval, but no conditions, prerequisites, or exclusions are stated.

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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