geocode_reverse"What address is at [lat,lng]" / "reverse geocode [coords]" / "what place is at this point" — reverse geocoding (coordinates → address / locality / region) via MapTiler.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | ||
| lon | Yes | ||
| limit | No | ||
| types | No | ||
| language | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "lat": 48.8566,
+ "lon": 2.3522
+ },
+ {
+ "language": "en",
+ "lat": 40.7128,
+ "limit": 3,
+ "lon": -74.006
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is clear. The description adds that it uses MapTiler service, but no additional behavioral traits (e.g., rate limits, return format, error handling) are disclosed beyond what annotations provide.
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 sentence with three example queries, efficiently conveying the tool's purpose. No unnecessary words or repetition—every part 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?
Given 5 parameters, 0% schema coverage, and no output schema, the description is incomplete. It fails to explain the 'limit', 'types', and 'language' parameters or the structure of the response. Annotations cover safety but not functional completeness. The description is adequate for a simple reverse geocoding tool but lacks depth to fully inform an agent about all options.
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 0% (no descriptions for parameters). The description mentions lat and lon implicitly via examples but does not explain the meaning of 'limit', 'types', or 'language' parameters. The examples show usage but lack semantic detail, leaving agents to infer parameter purposes from names alone.
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 converts coordinates to address/locality/region, using natural language examples ('What address is at [lat,lng]'). It distinguishes from sibling 'geocode' (forward geocoding) and other tools like 'coordinates_convert' and 'elevation'.
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 provides clear context through example queries (natural language, explicit 'reverse geocode'). It doesn't explicitly state when not to use this tool versus alternatives, but the sibling list includes 'geocode' for forward geocoding, implying the distinction.
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 have distinct purposes, but there is overlap, e.g., ask_pipeworx vs ask_pipeworx_grounded vs deep_research all for data lookup, and multiple Polymarket tools. Descriptions are detailed enough to distinguish, but the set is confusing.
Naming is highly inconsistent: snake_case (ai_visibility_check), verb_noun (resolve_entity), underscores (ask_pipeworx), and domain-specific prefixes (polymarket_). No consistent pattern across tools.
With 38 tools spanning geospatial, data lookup, prediction markets, memory, feedback, etc., the count is too high for a coherent server. Many tools are one-off and unrelated to the server's name (Maptiler).
The tool set covers many areas but lacks obvious gaps for each domain (e.g., MapTiler only has 4 tools). The overall surface is a collection of unrelated features, not a cohesive domain, so completeness is poor for any single purpose.