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Glama

What3words

by io.github.pipeworx-io

Words To Coords

words_to_coords
Read-onlyIdempotent

Convert a 3-word address ("filled.count.soap") to a lat/lon centre + 3m×3m bounding box.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordsYesThree-word address (dot-separated)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordsYesThe 3-word address input
countryYesCountry name containing the address
map_urlYesURL to view the address on what3words map
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: +[
+  {
+    "words": "filled.count.soap"
+  },
+  {
+    "words": "index.home.raft"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "country": {
+      "description": "Country name containing the address",
+      "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 input",
+      "type": "string"
+    }
+  },
+  "required": [
+    "words",
+    "latitude",
+    "longitude",
+    "square_sw",
+    "square_ne",
+    "country",
+    "nearest_place",
+    "map_url"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare the tool safe, idempotent, and non-destructive. The description adds value by specifying the output shape (center + 3m×3m bounding box). It does not disclose potential errors or precision, but for a simple read operation, this is sufficient.

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?

A single, front-loaded sentence with an example. Every word is necessary, no filler. Excellent conciseness.

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, rich annotations, full schema coverage, and presence of an output schema, the description provides all necessary context for correct invocation and understanding of the result.

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

Parameters3/5

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

Schema coverage is 100% and the schema already includes an example. The description's example is redundant. No additional meaning is provided for the 'words' parameter beyond what the schema offers, so it meets the baseline of 3.

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 uses a specific verb ('Convert') and resource ('3-word address'), clearly distinguishing this from its sibling 'coords_to_words' which does the reverse. The example and output details ('lat/lon centre + 3m×3m bounding box') solidify the purpose.

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 by stating the conversion direction, but does not explicitly mention when not to use or point to the sibling for the reverse. However, the context of a straightforward conversion makes the usage clear, and the sibling list provides the alternative.

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