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Verify a Company

by dev.hatchloop

lookup_us_contracts

Read-onlyIdempotent

Search US federal contract awards by company (recipient) name using the free USASpending.gov public API. Returns the top awards sorted by dollar amount: award ID, recipient name, award amount USD, awarding agency, NAICS code/description, and period of performance. Zero upstream cost, no key required. Data covers 2020-2026 procurement contracts.

EXAMPLE USER QUERIES THAT MATCH THIS TOOL: user: "Does Palantir have US government contracts?" -> call lookup_us_contracts({"company_name": "Palantir Technologies"}) user: "What federal contracts does Booz Allen Hamilton have?" -> call lookup_us_contracts({"company_name": "Booz Allen Hamilton", "max_results": 5}) user: "Find US import supplier data for Lockheed Martin" -> call lookup_us_contracts({"company_name": "Lockheed Martin"}) user: "Which government agencies are customers of SAIC?" -> call lookup_us_contracts({"company_name": "SAIC", "max_results": 10})

WHEN TO USE: Use when you need to know whether a company is a US federal contractor, which government agencies have awarded it contracts, the dollar value and NAICS sector of those contracts, and the period of performance. Useful for supplier due-diligence, vendor qualification, competitive intelligence, and import/trade research. The target demand: "us import data api", "supplier lookup api", "company trade records", "who has government contracts", "federal contractor search". WHEN NOT TO USE: Do not use for non-US companies (USASpending.gov only covers US federal contracts). Do not use as an exhaustive financial-risk or sanctions screen -- use verify_company_record and screen_sanctions for those. Do not use for state/local government contracts (federal only). COST: free - no key required LATENCY: ~1200ms

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_resultsNoMaximum number of contract awards to return (sorted by award amount descending). Default: 5, max: 10.
company_nameYesCompany or recipient name to search for in US federal contract awards (e.g. "Lockheed Martin", "Booz Allen Hamilton", "Palantir Technologies").

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, and the description adds valuable behavioral context: zero cost, no API key required, ~1200ms latency, 2020-2026 data coverage, and top-award sorting by dollar amount. There is no contradiction with annotations, and the additional details go beyond what structured annotations provide.

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 longer than average but well-structured with headers, examples, and explicit use/not-use guidance. Virtually every section adds decision-relevant value, though the target-demand keyword list and repeated examples could be trimmed without losing critical meaning. Overall, it is appropriately organized and front-loaded.

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?

Given two simple parameters, no output schema, and rich annotations, the description covers all essential context: data source, return fields, coverage years, cost/latency, limitations, and alternatives. An agent has everything needed to invoke the tool correctly and interpret what comes back.

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 company_name and max_results fully documented including default, min, max, and sorting behavior. The description itself does not add much parameter-level meaning beyond the schema, but example queries reinforce correct usage. Baseline 3 is appropriate because the schema already carries the semantic load.

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?

The description uses a specific verb and resource: 'Search US federal contract awards by company (recipient) name using the free USASpending.gov public API.' It also clearly states what is returned and differentiates itself from siblings by explicitly excluding sanctions and verification use cases. An agent can immediately understand the tool's unique role.

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

Usage Guidelines5/5

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

The description includes dedicated 'WHEN TO USE' and 'WHEN NOT TO USE' sections, naming specific alternatives like verify_company_record and screen_sanctions. It also gives concrete example user queries that map directly to parameter values. This is exemplary guidance for tool selection.

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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TDQS

A4.4/5.0
Disambiguation4/5

The core due-diligence tools are clearly distinct: registry verification, sanctions screening, and contract lookup each have separate purposes and boundaries. The only possible confusion is between get_status and get_outcome, but their descriptions clearly separate state polling from final result retrieval.

Naming Consistency4/5

Tool names mostly follow a snake_case verb_noun pattern such as get_status, preview_cost, screen_sanctions, and verify_company_record. The main deviation is self_test, and verbs vary across get, lookup, preview, screen, and verify, but there is no casing or style conflict.

Tool Count4/5

Seven tools is within the ideal range for a focused MCP server. However, several tools are generic infrastructure utilities rather than company-verification features, so the set is slightly less scoped than it could be.

Completeness3/5

The company-verification workflow is reasonably covered: legal existence, sanctions screening, and US federal contracts are all available. However, get_status and get_outcome reference async operations that no exposed tool creates, and preview_cost examples mention operations like send_message that are not present in the server, creating dead ends.

Resources