Reverse-geocode a longitude/latitude pair with the Census Geocoder (keyless). Returns the Census geographies (state, county, tract, block, congressional district) containing the point, with GEOIDs.
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude (y), e.g. 38.84 | |
| longitude | Yes | Longitude (x), e.g. -76.92 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior, so the description only needs to add context beyond that. It usefully discloses that the Census Geocoder is keyless and specifies the returned geography types. It does not discuss rate limits or US-only coverage, but for a simple read-only API this is a reasonable disclosure.
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 tightly written sentences, with the core action first and the return payload immediately following. Every clause carries useful information and there is no filler.
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 two-parameter, read-only reverse-geocoding call, the description covers the input, the data source, the keyless nature, and the output types. No output schema exists, so the explicit mention of returned geographies and GEOIDs compensates for that absence.
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 already documents both required parameters with descriptions and examples, so the baseline applies. The description adds no parameter-specific detail beyond restating that the input is a longitude/latitude pair.
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 uses a specific verb ('Reverse-geocode') plus a precise input resource (a longitude/latitude pair via the Census Geocoder) and names the output (Census geographies with GEOIDs). This clearly distinguishes it from forward-address and batch geocoding siblings like geocode_address and geocode_batch.
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?
It is obvious from the description that this tool is for a single coordinate pair, which implies a use case distinct from address-based or batch geocoding. It does not explicitly name alternatives or state when not to use it, so it falls short of the strongest level.
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.