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What3words

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

Schema Changelog

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

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate safety (readOnly, idempotent, non-destructive). Description adds process details: fetches page, extracts title/description/links, emits standard markdown. 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 dense sentences plus a bullet list of use cases. No filler, each part adds value.

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?

Simple tool with strong annotations and clear description. Covers purpose, output format, usage scenarios. No output schema needed.

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 description coverage is 100%, so the schema documents both parameters. Description adds minimal extra (default and max for max_links). Adequate but not enhanced.

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 verb 'Generate' and the resource 'llms.txt file for any URL'. It explains the benefit for AI crawlers and distinguishes from sibling tools like scan_competitor_ai_presence.

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?

Provides explicit use cases: indexing a client's site, drafting your own, or auditing competitors. Lacks explicit when-not-to-use, but the context is clear.

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