DataNexus MCP

compliance_check_sam_exclusion

Read-onlyIdempotent

Check whether an entity is on the US federal exclusions list (debarred from government contracts). Read-only. No side effects. Idempotent. US only. name_or_ein: Entity name or 9-digit EIN with or without dash e.g. Acme Corp or 13-1234567. Required. Name match is fuzzy — verify EIN for exact results. Returns excluded: true/false, exclusion type, and exclusion dates if found. Use this before awarding federal contracts or grants. Use govcon_search_contract_awards instead to find what contracts an entity has won. Verified source: SAM.gov. 24-hour cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="compliance_check_sam_exclusion", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name_or_ein`	Yes	Entity name or EIN to check SAM exclusions. Required.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.8/5.0

Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint; description confirms no side effects, idempotent, and adds US-only, 24-hour cache, fuzzy name matching, and return fields. Significantly supplements annotation context.

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?

Description is dense and front-loaded with purpose. Each sentence provides essential information, though it could be slightly more concise. Still very efficient.

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 low complexity (1 param, annotations present, output schema implied), description covers all necessary context: return fields, caveats (fuzzy match, cache), and fallback instruction for reporting gaps.

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

Parameters4/5

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

Schema description coverage is 100%, but description adds examples (Acme Corp or 13-1234567) and clarifies fuzzy matching, adding value beyond schema's minimal description.

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 clearly states the tool checks if an entity is on the US federal exclusions list, using specific verbs and resources. It distinguishes itself from sibling tool 'govcon_search_contract_awards' by mentioning the alternative.

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 says to use before awarding federal contracts/grants, and to use 'govcon_search_contract_awards' instead for contract search. Provides clear when-to-use and when-not.

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.

Resources

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