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by com.datanexusmcp

govcon_search_contract_awards

Read-onlyIdempotent

Search government contract awards by keyword, agency, and date range.

keyword: Contract scope e.g. "cybersecurity software". agency: Awarding agency e.g. "Department of Defense". Optional. date_from: Earliest award date ISO 8601 e.g. "2024-01-31". Optional. jurisdiction: "US", "EU", or "UK". Default "US".

Returns: award amounts, recipient vendors, NAICS codes, award dates. Use govcon_fetch_vendor_contract_history for all contracts by a specific vendor. Use govcon_fetch_open_solicitations for active bids, not past awards. Source: USASpending.gov + SAM.gov. 4-hour cache.

Example: search_contract_awards(keyword="cybersecurity software", agency="Department of Defense")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agencyNoAwarding agency name e.g. Department of Defense. Optional.
keywordYesSearch terms describing the contract scope e.g. cybersecurity software. Required.
date_fromNoEarliest award date ISO 8601 e.g. 2024-01-31. Optional.
jurisdictionNoJurisdiction: US, EU, or UK. Default US. Optional.US

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Annotations indicate readOnly and non-destructive. Description adds cache duration (4 hours), data source (USASpending.gov + SAM.gov), and return fields. Some behavioral details like pagination or max results are missing.

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

Conciseness5/5

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

Well-organized with a summary sentence, parameter list, return info, sibling guidance, and source details. No redundant text.

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 the rich annotations and output schema, the description covers purpose, parameters, return content, data source, caching, and comparisons to siblings. It is fully adequate.

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 coverage is 100%, so baseline is 3. The description repeats parameter descriptions and adds an example, but does not introduce new semantics beyond the schema.

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?

Clearly states the tool searches government contract awards with keyword, agency, and date range filters. The example and sibling differentiations reinforce the specific verb and resource.

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?

Explicitly tells when to use alternatives: govcon_fetch_vendor_contract_history for vendor-specific queries and govcon_fetch_open_solicitations for active bids. This leaves no ambiguity.

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

A4.1/5.0
Disambiguation4/5

Tools are grouped into clear domain prefixes (compliance, domain, frontend_security, etc.) with distinct purposes. Minor overlap exists between frontend_security_detect_typosquatting and security_detect_typosquatting, but descriptions clarify the different scope.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern with snake_case. Irregularities like 'fetch' vs 'audit' and two 'detect_typosquatting' tools exist, but overall naming is predictable within domains.

Tool Count3/5

55 tools is high for a single server given the breadth of domains. Some redundancy (e.g., two typosquatting tools) suggests possible trimming, but the count is justified by the wide coverage.

Completeness4/5

The tool surface covers key operations across domains like compliance, domain, security, legal, and nonprofit. Minor gaps exist, such as limited frontend audit beyond package.json and no general-purpose code scanning.

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