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Glama

What3words

by io.github.pipeworx-io

Coords To Words

coords_to_words
Read-onlyIdempotent

Convert a lat/lon to a what3words 3-word address. Defaults to English; pass a 2-letter language code for other languages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNo2-letter language code (default "en")
latitudeYesLatitude
longitudeYesLongitude

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordsYesThe 3-word address (e.g., 'filled.count.soap')
countryYesCountry name containing the address
map_urlYesURL to view the address on what3words map
languageYesLanguage code used for the words
latitudeYesLatitude of the 3m×3m square center
longitudeYesLongitude of the 3m×3m square center
square_neYesNortheast corner of bounding box
square_swYesSouthwest corner of bounding box
nearest_placeYesNearest place name to the address

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "latitude": 51.520847,
+    "longitude": -0.195521
+  },
+  {
+    "language": "es",
+    "latitude": 40.7128,
+    "longitude": -74.006
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "country": {
+      "description": "Country name containing the address",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "language": {
+      "description": "Language code used for the words",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "latitude": {
+      "description": "Latitude of the 3m×3m square center",
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "longitude": {
+      "description": "Longitude of the 3m×3m square center",
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "map_url": {
+      "description": "URL to view the address on what3words map",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "nearest_place": {
+      "description": "Nearest place name to the address",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "square_ne": {
+      "description": "Northeast corner of bounding box",
+      "properties": {
+        "lat": {
+          "description": "Northeast corner latitude",
+          "type": "number"
+        },
+        "lng": {
+          "description": "Northeast corner longitude",
+          "type": "number"
+        }
+      },
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "square_sw": {
+      "description": "Southwest corner of bounding box",
+      "properties": {
+        "lat": {
+          "description": "Southwest corner latitude",
+          "type": "number"
+        },
+        "lng": {
+          "description": "Southwest corner longitude",
+          "type": "number"
+        }
+      },
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "words": {
+      "description": "The 3-word address (e.g., 'filled.count.soap')",
+      "type": [
+        "string",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "words",
+    "language",
+    "latitude",
+    "longitude",
+    "square_sw",
+    "square_ne",
+    "country",
+    "nearest_place",
+    "map_url"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate idempotent, read-only, non-destructive behavior. The description adds that the tool defaults to English and accepts a 2-letter language code, which is beyond what annotations provide. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no wasted words. The most important information (purpose and default language) comes first. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and the presence of an output schema, the description is complete. It covers the core transformation, default behavior, and optional parameter usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the output format (3-word address) and the language parameter behavior (default English, 2-letter code), which complements the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the verb (convert), resource (lat/lon to what3words 3-word address), and distinguishes from the sibling tool 'words_to_coords' which does the reverse. It also clarifies default behavior and language option.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context—converting coordinates to words—and the sibling list includes the inverse tool, providing indirect guidance. However, it does not explicitly state when not to use it or suggest alternatives for non-what3words cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All What3words toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

There is significant overlap among the meta-query tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions, and validate_claim all route to the same underlying data catalog with only subtle differences in grounding or scope. Company-focused tools like entity_profile, compare_entities, recent_changes, and resolve_entity also share fuzzy boundaries. The four what3words tools are clearly distinct, but they sit awkwardly beside a much larger, partially redundant Pipeworx/prediction-market cluster.

Naming Consistency3/5

All tool names use snake_case, which provides a base level of consistency, but the naming patterns vary widely: some are verb_noun (list_languages, recall, forget), some are X_to_Y (coords_to_words, words_to_coords), some are brand-prefixed (pipeworx_*, polymarket_*), and some are bare concepts (deep_research, entity_profile, autosuggest). The ask_pipeworx family is internally consistent, as are the polymarket_* tools, but the overall set lacks a single predictable convention.

Tool Count2/5

35 tools is well above the 25-tool threshold, and the server is named What3words when only 4 of the 35 tools actually belong to that geocoding domain. Even interpreted as a general data platform, 35 tools with a heavily overlapping meta-tool layer feels bloated rather than well-scoped. The what3words-specific surface would be appropriately sized at 4-5 tools on its own.

Completeness3/5

For what3words specifically, the surface is complete: coords_to_words, words_to_coords, autosuggest, and list_languages cover the core bidirectional conversion plus discovery. However, for the broader domain the server actually serves, there are notable gaps such as no direct resolve-by-pipeworx://-URI tool and no open-web search, despite citations and external data being advertised as fetchable. The mix of geocoding, data lookup, prediction markets, memory, and subscriptions makes it unclear what complete coverage would even mean.

Resources