here_reverse_geocodeTurn coordinates into the nearest street address (reverse geocoding) via HERE — "what address is at 40.7484, -73.9857". Returns the normalized address, place title, and distance to the matched location.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude, e.g. 40.7484. | |
| lng | Yes | Longitude, e.g. -73.9857. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "lat": 40.7484,
+ "lng": -73.9857
+ },
+ {
+ "lat": 48.8584,
+ "lng": 2.2945
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint, so the safety profile is clear. Description adds useful behavioral information: 'Returns the normalized address, place title, and distance to the matched location'. No contradictions or extra behavioral traits 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?
Two sentences: first states purpose, second states return values. Front-loaded with key information, no wasted words. Extremely concise and well-structured.
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 the tool's simplicity (2 simple params, no output schema), the description fully covers its operation. It explains inputs and outputs adequately, with no missing context.
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 coverage is 100%, and parameters are well-described in the schema. The description mentions coordinates and provides examples, but does not add new meaning beyond the schema properties.
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?
Description clearly states the tool's purpose: 'Turn coordinates into the nearest street address (reverse geocoding)'. It provides an example query format and distinguishes from siblings by naming 'reverse geocoding' (as opposed to forward geocoding in here_geocode).
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?
Usage is evident from the purpose and example. It explains what the tool does and gives a concrete query example. However, no explicit when-not or alternative tool guidance is provided.
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 tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and suggest_questions all route questions to the same underlying data catalog, making it hard to pick the right one. The Polymarket-related tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also have overlapping discovery and analysis purposes.
Many tools use descriptive snake_case, and the ask_pipeworx family shares a clear prefix, but the set mixes generic memory verbs (remember, recall, forget), brand-prefixed tools (here_*, pipeworx_*), and standalone names like bet_research and scan_dependency. There is no consistent verb_noun pattern across the whole server.
35 tools is heavy for a single MCP server, and a large portion are meta-tools layered over the same 5,752-tool catalog (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad intentional scope explains the count, but the tool surface feels bloated and harder to navigate than it needs to be.
The domain is unusually broad—data querying, entity resolution, comparison, monitoring, memory, geolocation, prediction markets, dependency scanning—and the set covers most workflows end to end. Minor gaps exist, like no direct pipeworx:// citation fetcher and no update/list/delete pattern for entity profiles, but the core user journeys are well supported.