reverse_geocodeConvert coordinates to a physical address. Returns street address, city, country, and postal code. Use to identify locations from lat/lng pairs.
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude | |
| longitude | Yes | Longitude |
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Detailed address components as key-value pairs | |
| display_name | Yes | Formatted address string |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "latitude": 40.7128,
+ "longitude": -74.006
+ },
+ {
+ "latitude": 48.8584,
+ "longitude": 2.2945
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "address": {
+ "description": "Detailed address components as key-value pairs",
+ "type": "object"
+ },
+ "display_name": {
+ "description": "Formatted address string",
+ "type": "string"
+ }
+ },
+ "required": [
+ "display_name",
+ "address"
+ ],
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {
- "lat": 48.8584,
- "lon": 2.2945
- },
- {
- "lat": 40.7128,
- "lon": -74.006
- }
-]Output schema / (root)Previous value: -{
- "properties": {
- "address": {
- "additionalProperties": {
- "type": "string"
- },
- "description": "Address components as key-value pairs",
- "type": "object"
- },
- "display_name": {
- "description": "Human-readable address or place name",
- "type": "string"
- },
- "importance": {
- "description": "Importance score of the place",
- "type": "number"
- },
- "lat": {
- "description": "Latitude in decimal degrees",
- "type": "number"
- },
- "lon": {
- "description": "Longitude in decimal degrees",
- "type": "number"
- },
- "osm_id": {
- "description": "OpenStreetMap object ID",
- "type": "number"
- },
- "osm_type": {
- "description": "OpenStreetMap object type (node, way, relation)",
- "type": "string"
- },
- "place_id": {
- "description": "Unique place identifier",
- "type": "number"
- },
- "type": {
- "description": "Place type (e.g., landmark, city, road)",
- "type": "string"
- }
- },
- "required": [
- "place_id",
- "osm_type",
- "osm_id",
- "lat",
- "lon",
- "display_name",
- "type",
- "importance"
- ],
- "type": "object"
-}New value: +nullOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "address": {
+ "additionalProperties": {
+ "type": "string"
+ },
+ "description": "Address components as key-value pairs",
+ "type": "object"
+ },
+ "display_name": {
+ "description": "Human-readable address or place name",
+ "type": "string"
+ },
+ "importance": {
+ "description": "Importance score of the place",
+ "type": "number"
+ },
+ "lat": {
+ "description": "Latitude in decimal degrees",
+ "type": "number"
+ },
+ "lon": {
+ "description": "Longitude in decimal degrees",
+ "type": "number"
+ },
+ "osm_id": {
+ "description": "OpenStreetMap object ID",
+ "type": "number"
+ },
+ "osm_type": {
+ "description": "OpenStreetMap object type (node, way, relation)",
+ "type": "string"
+ },
+ "place_id": {
+ "description": "Unique place identifier",
+ "type": "number"
+ },
+ "type": {
+ "description": "Place type (e.g., landmark, city, road)",
+ "type": "string"
+ }
+ },
+ "required": [
+ "place_id",
+ "osm_type",
+ "osm_id",
+ "lat",
+ "lon",
+ "display_name",
+ "type",
+ "importance"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "lat": 48.8584,
+ "lon": 2.2945
+ },
+ {
+ "lat": 40.7128,
+ "lon": -74.006
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool safe (readOnlyHint=true, idempotentHint=true, destructiveHint=false). The description adds that the output includes street address, city, country, and postal code, but this is likely already specified in the output schema. No additional behavioral traits (e.g., coordinate system, accuracy, rate limits) are disclosed, so the description adds marginal value beyond 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?
Two concise sentences, front-loaded with the operation and result. No filler words or redundant information.
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 with comprehensive annotations and an output schema, the description is sufficient. It states the purpose, inputs, and output fields, and differentiates from the sibling geocode tool.
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 have descriptions in the schema ('Latitude', 'Longitude'), covering 100% of parameters. The description's 'lat/lng pairs' adds no new detail about formats, ranges, or required coordinate systems. Baseline 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 uses a specific verb ('Convert') and names the resource ('coordinates to a physical address'), clearly distinguishing it from the sibling 'geocode' tool which does the opposite direction. This is a precise statement of the tool's function.
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 final sentence explicitly states the intended use case ('Use to identify locations from lat/lng pairs'), providing clear context. It doesn't explicitly mention alternatives or when not to use, but the sibling list and the 'reverse' naming make the contrast implicit.
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.
Multiple tools serve overlapping purposes, especially ask_pipeworx and ask_pipeworx_beta (explicitly identical) and ask_pipeworx_grounded/deep_research/validate_claim for fact retrieval. Even with detailed descriptions, an agent could easily misselect among data-query tools or among the five Polymarket analysis tools.
Tool names mix verb-first (ask_pipeworx, compare_entities), noun-first (polymarket_edges, entity_profile), and single-word verbs (geocode, forget), with no strict verb_noun pattern. However, all names are snake_case and mostly descriptive, so the inconsistency is moderate.
36 tools is far beyond the typical well-scoped range of 3-15, and the feature set spans research, memory, subscriptions, prediction markets, and geo utilities. Many tools are meta-tools (discover_tools, suggest_questions) that could be consolidated, making the surface feel bloated.
For the apparently broad domain of data research and prediction markets, the toolset covers most needs with parallel research, grounding, claim verification, memory, and subscription lifecycle. Minor gaps exist—like a direct way to fetch arbitrary raw data or a unified list of all tools—but agents can generally work around them.