Full IRS EO BMF record for one organization by EIN, with the coded fields (subsection, foundation status, deductibility, EO status, ruling date) decoded to human-readable labels. Includes address, NTEE code, and the most recent reported asset/income/revenue figures.
| Name | Required | Description | Default |
|---|---|---|---|
| ein | Yes | Employer Identification Number (EIN). Accepts 9 digits with or without a dash, e.g. "13-1837418" or "131837418". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnly, idempotent, and non-destructive behavior. The description adds useful processing context: coded BMF fields are decoded to human-readable labels and financial figures are described as 'most recent reported,' which sets accurate expectations. No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two dense sentences with the main deliverable front-loaded and supporting output details following. No filler, repetition, or unnecessary restating of schema or annotation content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description carries the burden of explaining return contents. It names the record type, decoded coded fields, address, NTEE code, and financial figures, which is sufficient for selection and invocation, though still high-level for a 'full' record.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already documents EIN format, dash acceptance, and required status. The description adds no further parameter semantics beyond saying 'by EIN,' so it appropriately relies on the schema to carry the parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (full IRS EO BMF record) and scope (one organization by EIN), and enumerates key decoded/included fields. Compared to siblings like nonprofit_lookup_ein it implies a fuller record, but it never names or contrasts that sibling, so differentiation is implicit rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied rather than explicitly stated: the description tells the agent this is for a known EIN and produces a full record, but it gives no explicit when-to-use or when-not-to-use guidance. Given the presence of nonprofit_lookup_ein and search siblings, an agent must infer when to choose this over them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.