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Dwds

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context: fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This goes beyond annotations without contradiction.

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 concise: two short paragraphs, first covering purpose and process, second listing use cases. No superfluous words, front-loaded with essential information.

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

Completeness4/5

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

Given only two parameters, no output schema, and rich annotations, the description covers key behavioral aspects and output format. It could mention error handling for invalid URLs, but overall it is sufficient for an agent to understand and invoke the tool correctly.

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 baseline is 3. The description adds context about output format and extraction process but does not add semantic meaning beyond what the schema already provides for parameters (url, max_links). No extra parameter-level detail is given.

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 generates a production-ready llms.txt file for any URL, specifying the verb 'generate' and the resource 'llms.txt'. It also distinguishes itself from siblings by focusing on AI crawler indexing, with explicit use cases for clients, own projects, or competitors.

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 provides explicit use cases: getting a client's site indexed, drafting for own project, or auditing a competitor's AI visibility. However, it does not explicitly mention when not to use or compare to sibling tools like 'scan_competitor_ai_presence', leaving some room for ambiguity.

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

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TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical; the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) also overlap. The server name suggests a German dictionary, but most tools are unrelated, causing confusion about the set's focus.

Naming Consistency2/5

Tool names lack a consistent pattern: some use verb_noun (ask_pipeworx, compare_entities), some noun_noun (ai_visibility_check, dwds_frequency), some single words (forget, lemma), and some are acronyms (kwic). This mixed convention makes prediction of tool names difficult.

Tool Count3/5

At 36 tools, the set is large but not extreme. However, it attempts to cover too many domains (German language, prediction markets, AI visibility, npm packages, etc.), making it feel bloated and unfocused for a server named 'Dwds'.

Completeness2/5

For the German dictionary focus implied by the server name, tools are minimal (dwfs_frequency, lemma, snippet) and two are retired. For the broader data platform, there are gaps like no dedicated SEC filing search tool, relying on generic ask_pipeworx. The set feels incomplete for both intended purposes.

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