Geocode up to 10 one-line US addresses in a single call. Returns one block per input address (matched address, coordinates, and county/tract GEOIDs). For large jobs the Census batch file API supports up to 10k rows; this tool covers small ad-hoc batches.
| Name | Required | Description | Default |
|---|---|---|---|
| addresses | Yes | Array of one-line address strings. Max 10. |
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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral detail beyond annotations by specifying the return shape (one block per input address with matched address, coordinates, and county/tract GEOIDs) and the batch limit of 10. No contradiction with annotations.
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?
Three sentences with no filler. The core action and batch limit are front-loaded, return shape is stated second, and the alternative API guidance is a compact final sentence. 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 read-only, idempotent tool with a single parameter and 100% schema coverage, the description covers the key operational constraints: max 10 addresses, US-only addresses, one output block per input, and the boundary against the larger Census API. It lacks details like error behavior or address normalization, but those are not critical for a simple geocoding call with no output schema.
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 description coverage is 100% for the single 'addresses' parameter, so the schema already defines an array of one-line address strings with max 10. The description adds value by clarifying what each returned block contains, which indirectly explains how the input addresses map to outputs. Some format expectations (e.g., address formatting guidelines) are not detailed, but the schema plus description are largely sufficient.
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 states the tool geocodes up to 10 one-line US addresses in a single call, with a specific verb (geocode), resource (addresses), and explicit constraints. It also distinguishes itself from the larger Census batch file API, helping an agent differentiate it from similar geocoding tools.
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 explicitly frames when to use this tool ('small ad-hoc batches') versus when to use the Census batch file API ('up to 10k rows'). It doesn't name a sibling tool, but the guidance is sufficient for an agent to select it appropriately against the broader sibling list.
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.