geocodeConvert an address or place name to up to 5 matching coordinates via OpenStreetMap Nominatim. Returns latitude, longitude, display_name, and place type for each result.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Address or place name to geocode |
| Name | Required | Description | Default |
|---|---|---|---|
| results | Yes | List of geocoding results |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "1600 Pennsylvania Avenue, Washington DC"
+ },
+ {
+ "query": "Eiffel Tower, Paris"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "results": {
+ "description": "List of geocoding results",
+ "items": {
+ "properties": {
+ "display_name": {
+ "description": "Formatted address or place name",
+ "type": "string"
+ },
+ "latitude": {
+ "description": "Latitude coordinate",
+ "type": "number"
+ },
+ "longitude": {
+ "description": "Longitude coordinate",
+ "type": "number"
+ },
+ "type": {
+ "description": "OSM type of result",
+ "type": "string"
+ }
+ },
+ "required": [
+ "latitude",
+ "longitude",
+ "display_name",
+ "type"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "results"
+ ],
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {
- "query": "1600 Pennsylvania Avenue, Washington DC"
- },
- {
- "query": "Eiffel Tower, Paris"
- }
-]Output schema / (root)Previous value: -{
- "properties": {
- "results": {
- "description": "List of geocoding results",
- "items": {
- "properties": {
- "display_name": {
- "description": "Formatted address or place name",
- "type": "string"
- },
- "latitude": {
- "description": "Latitude coordinate",
- "type": "number"
- },
- "longitude": {
- "description": "Longitude coordinate",
- "type": "number"
- },
- "type": {
- "description": "OSM type of result",
- "type": "string"
- }
- },
- "required": [
- "latitude",
- "longitude",
- "display_name",
- "type"
- ],
- "type": "object"
- },
- "type": "array"
- }
- },
- "required": [
- "results"
- ],
- "type": "object"
-}New value: +nullOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "results": {
+ "description": "List of geocoding results",
+ "items": {
+ "properties": {
+ "display_name": {
+ "description": "Formatted address or place name",
+ "type": "string"
+ },
+ "latitude": {
+ "description": "Latitude coordinate",
+ "type": "number"
+ },
+ "longitude": {
+ "description": "Longitude coordinate",
+ "type": "number"
+ },
+ "type": {
+ "description": "OSM type of result",
+ "type": "string"
+ }
+ },
+ "required": [
+ "latitude",
+ "longitude",
+ "display_name",
+ "type"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "results"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "query": "1600 Pennsylvania Avenue, Washington DC"
+ },
+ {
+ "query": "Eiffel Tower, Paris"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive properties. The description adds useful behavioral context: the external service (Nominatim), a result limit of up to 5, and the specific return fields. This goes beyond the annotation baseline without contradicting it.
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 core action, followed by constraints and return fields. Every word earns its place, with no fluff.
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 single-parameter read-only tool, the description covers purpose, data source, result limit, and return shape. Combined with the existing output schema and annotations, it leaves no significant gaps.
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?
The schema fully documents the single query parameter with an identical description, so schema coverage is 100%. The description reinforces the input type but adds no new semantic meaning beyond what the schema already provides.
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?
Clearly states the tool converts an address or place name to up to 5 matching coordinates, specifying the data source (OpenStreetMap Nominatim). This distinguishes it from reverse_geocode and other siblings.
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 clearly implies when to use it: when you have an address or place name and need coordinates. It doesn't explicitly name alternatives like reverse_geocode, but the context is unmistakable.
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.