US reverse geocoding (Census) — Coordinates to US state, county, tract and block geography. Source: US Census Bureau. JSON. Price: $0.002 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | latitude | |
| lon | Yes | longitude |
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 establish the tool as read-only and non-destructive. The description adds valuable behavioral context: Census as the authoritative source, JSON as the response format, and a unit price of $0.002 USDC, which helps an agent assess cost and trustworthiness. It does not disclose rate limits or coordinate format expectations, but it also does not contradict the 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?
The description is extremely concise and well-structured: purpose is front-loaded, followed by source, format, and price in short declarative fragments. Each segment adds distinct information with no filler or redundancy.
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 tool, the description provides sufficient context: geographic scope, source, output geography, response format, and cost. With no output schema, it still hints at what the response contains, though field names and coordinate precision are unspecified. Overall it is complete enough for correct selection and invocation.
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?
Both parameters are fully documented in the schema with simple descriptions ('latitude', 'longitude'), giving 100% schema coverage. The description does not add parameter-level detail such as coordinate format (e.g., decimal degrees) or valid ranges, so the baseline score of 3 is appropriate.
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 defines the operation: coordinates are converted into US state, county, tract, and block geography using Census data. The 'Coordinates to ...' phrasing states the resource and output scope, distinguishing it from forward geocoding tools like geo_geocode. Although it opens with a title-like phrase, the full description conveys a specific, actionable purpose.
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 sets geographic scope (US) and source (Census Bureau), allowing an agent to infer this is for US reverse-geocoding tasks. However, it does not mention sibling tools such as geo_geocode or state when not to use this tool, so usage guidance is implicit rather than explicit.
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 are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.