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Glama

What3words

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

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

  1. Changed5 schema fields changed
    • addedInput schema / properties / description
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / q
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • changedInput schema / properties / query / description
      Previous value: -"Natural language description of what you want to do (e.g., \"analyze housing market trends\", \"look up FDA drug approvals\", \"find trade data between countries\")"New value: +"Natural language description of what you want to do (e.g., \"analyze housing market trends\", \"look up FDA drug approvals\", \"find trade data between countries\"). Accepts task, q, description, search as aliases."
    • addedInput schema / properties / search
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / task
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "look up FDA drug approvals"
+  },
+  {
+    "query": "analyze housing market trends"
+  }
+]
  3. 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 readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing the return format (top-N tools with full schemas and curated examples) and stating results are ready to call directly, which is not captured in annotations.

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?

The description is compact yet comprehensive: it states purpose, lists use scenarios, details return value, and provides usage guidance—all in a few well-structured sentences. Every sentence adds value, and key information is front-loaded.

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?

The description fully covers what the tool returns (top-N tools with names, descriptions, full input schemas, examples, and readiness to call) and when to use it. Given no output schema, this provides all necessary context. The tool is simple (one required param), making this complete.

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?

The description explains that the query parameter is a 'natural language description' and provides concrete examples (e.g., 'analyze housing market trends'). It notes multiple aliases (task, q, search, description) and the limit parameter's defaults and max, adding meaning beyond the schema.

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 clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists many specific domains (SEC filings, financials, FDA drugs, etc.) and distinguishes itself from siblings by focusing on tool discovery rather than direct data retrieval.

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 explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' While it doesn't name alternative tools, the use case is clearly scoped to situations requiring browsing multiple tool options.

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