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lzinga

US Government Open Data MCP

by lzinga

usa_spending_by_recipient

Read-onlyIdempotent

Find top federal spending recipients by state, agency, award type, or fiscal year to identify which companies and organizations receive the most government funds.

Instructions

Get the top recipients (companies, organizations) of federal spending. Use state and agency filters to narrow results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of recipients (default: 25)
stateNoTwo-letter state code, e.g. 'CA', 'TX'
agencyNoAwarding agency name, e.g. 'Department of Energy'
award_typeNoAward type filter
fiscal_yearNoFiscal year (default: current)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.9.14
    • addedInput schema / properties / limit / default
      Added value: +25
  2. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  3. First observedv2026.3.9

TDQS

A3.5/5.0
Behavior3/5

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds minimal extra behavioral context beyond stating it 'gets' data. It does not mention response format, ordering, or any limitations. Since annotations cover the risk aspects, a 3 is appropriate given the low additional value.

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?

The description is two sentences with no wasted words. It front-loads the verb and resource and immediately mentions the key usage hint. It is efficiently written, though it could include a bit more context without becoming verbose.

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 read-only list tool with no output schema, the description provides sufficient context: it explains what is returned (top recipients) and how to refine (state/agency). It does not detail ranking criteria or response structure, but given the simplicity and annotations, the essentials are covered.

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?

The schema documents all 5 parameters with descriptions, achieving 100% coverage. The description reinforces usage of 'state' and 'agency' filters but does not add new semantic information beyond the schema. Baseline 3 is appropriate since the schema is complete.

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 clearly states the verb 'Get' and the resource 'top recipients (companies, organizations) of federal spending'. It is specific and distinct from the sibling tools by name, though it does not explicitly differentiate itself from them. The mention of filters adds clarity, but the purpose is still clear.

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

Usage Guidelines3/5

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

The description advises to 'Use state and agency filters to narrow results', which provides useful usage context. However, it does not explicitly state when to prefer this tool over siblings like usa_spending_by_state or usa_spending_by_agency, nor does it mention any exclusions or alternatives. The guidance is implicit rather than explicit.

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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