DataNexus MCP

nonprofit_fetch_nonprofit_by_ein

Read-onlyIdempotent

Fetch IRS 990 filing data for any US nonprofit by EIN. Read-only. No side effects. Idempotent. US only. ein: 9-digit Employer ID with or without dash, e.g. 46-5734087 or 465734087. Required. Returns name, revenue, expenses, assets, NTEE code, and mission from the most recent 990 filing. Use this when you have the exact EIN. Use nonprofit_search_nonprofits_by_name instead when you only have a name. Verified source: IRS EO BMF + IRS TEOS. 7-day cache. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="nonprofit_fetch_nonprofit_by_ein", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ein`	Yes	EIN in format XX-XXXXXXX e.g. 46-5734087. 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 already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description reinforces these with explicit statements (read-only, no side effects, idempotent) and adds context (US only, 7-day cache, specific return fields, feedback mechanism). No contradictions.

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 informative but somewhat lengthy. However, every sentence serves a purpose (purpose, usage, behavior, params, fallback). Front-loaded with core function. Could be slightly more concise but effective overall.

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 presence of an output schema (not shown but indicated) and the tool's simplicity, the description covers all necessary aspects: purpose, parameters, behavioral traits, usage guidelines, and fallback. No 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 coverage is 100% with one parameter (ein) well-described in schema. The description adds value by clarifying the format (with or without dash) and providing examples, improving practical usability.

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 fetches IRS 990 filing data for US nonprofits by EIN. It distinguishes itself from the sibling tool nonprofit_search_nonprofits_by_name, which is used when only a name is available.

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 states when to use this tool (when you have exact EIN) and when to use an alternative (nonprofit_search_nonprofits_by_name for name-only queries). Also provides context on data source, caching, and a feedback fallback.

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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