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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds meaningful context: calls are keyless, uses the Census Geocoder, and returns a specific set of geographies. It does not detail rate limits or error behavior, but the annotations carry the safety profile and the description adds useful operational context.
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?
The description is a single focused sentence that front-loads the operation and includes only high-value details: reverse geocoding, coordinate inputs, source, keyless access, and return values. There is no redundant phrasing or 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 simple two-parameter, read-only tool, this description is complete: it explains the inputs, source, authentication requirement (none), and expected output. The absence of an output schema is mitigated by the explicit statement that Census geographies and GEOIDs are returned.
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 descriptions fully cover both parameters with examples (latitude and longitude). The description reinforces that the tool expects a coordinate pair but adds no further meaning beyond the schema, which already names and explains both fields. A baseline of 3 is appropriate given 100% schema coverage.
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 states a specific verb ('Reverse-geocode'), a specific resource ('a longitude/latitude pair'), and the data source ('Census Geocoder'). It also names the return values (Census geographies with GEOIDs), distinguishing it from forward-geocoding tools like geocode_address.
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?
The description makes the usage context clear: use this when you have coordinates and need the containing Census geographies. It does not explicitly name alternatives or state when not to use it, but the 'Reverse-geocode' phrasing and coordinate input provide enough context to differentiate it from address-based or batch geocoding siblings.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.