Look up a US tax-exempt organization by exact EIN from the IRS Exempt Organizations Business Master File (~1.27M orgs). Returns name, address, IRC subsection, and current EO status. Use nonprofit_details for the fully decoded record.
| 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 declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description is not needed for safety. It adds useful behavioral context by naming the IRS dataset, the dataset size, and the specific fields returned (name, address, IRC subsection, current EO status).
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 sentences with no filler: the core lookup action and source come first, returned fields follow, and the alternative tool is mentioned last. Every sentence earns its place.
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?
For a one-parameter lookup tool with read-only annotations, the description covers the source, match criteria, dataset scope, and return fields. Even without an output schema, this is enough for an agent to invoke the tool correctly and interpret the result.
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?
The schema provides 100% coverage of the single 'ein' parameter, including format examples with and without dashes. The description's phrase 'exact EIN' only restates part of the schema and does not add meaningful new parameter-level guidance.
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?
States a specific verb and resource ('Look up a US tax-exempt organization by exact EIN') and names the IRS Exempt Organizations Business Master File as the source. It also distinguishes itself from nonprofit_details by noting that tool provides the 'fully decoded record.'
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?
Explicitly points to nonprofit_details as the alternative when a fully decoded record is needed, which helps an agent choose correctly. It does not mention other sibling search tools like nonprofit_search_name or nonprofit_search_location, but the exact-EIN scope makes the intended use clear.
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.