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LiveDataLink

by io.github.blackboxfoundry

nonprofit_search_name

Read-onlyIdempotent

Fuzzy-search tax-exempt organizations by name, optionally filtered to a US state. Tolerant of word reordering and minor spelling differences. Returns ranked matches with EIN, location, and IRC subsection. Use the returned EIN with nonprofit_details or nonprofit_lookup_ein.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesOrganization name or partial name to search for.
limitNoMax matches to return (default 10, max 50).
stateNoOptional 2-letter US state/territory code to narrow results, e.g. "NY", "TX", "CA".

TDQS

A4.3/5.0
Behavior4/5

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses important behavioral traits: tolerance to word reordering and minor spelling differences, ranked result ordering, and the specific fields returned. This gives an agent realistic expectations about matching behavior that the annotations alone do not convey.

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?

The description is three sentences and every sentence earns its place: purpose, matching behavior, and downstream usage. The most important information is front-loaded, and there is no filler or repetition of schema details.

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 absence of an output schema, the description compensates by stating what the ranked matches contain: EIN, location, and IRC subsection. It also covers the optional state filter and gives the next-step action with related tools. For a low-complexity search tool, this is complete enough for an agent to invoke it correctly.

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 description coverage is 100%, so the baseline is 3. The description adds only marginal parameter context: it restates that state filtering is optional, which the schema already documents. It does not clarify the meaning of limit or name beyond their schema descriptions, so no score above baseline is warranted.

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 opens with a specific verb and resource: 'Fuzzy-search tax-exempt organizations by name,' clearly distinguishing this from sibling tools like nonprofit_details and nonprofit_search_location. It also states the core behavior (fuzzy matching, state filtering) and the nature of the results (ranked matches with EIN, location, IRC subsection), making the tool's purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use it: when you need to find an organization by name and may want to narrow to a state. It also routes downstream behavior by explicitly saying to use the returned EIN with nonprofit_details or nonprofit_lookup_ein. It does not explicitly mention nonprofit_search_location as an alternative, but the 'by name' 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.

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TDQS

B3.2/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources