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Worms

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).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that it fetches pages, extracts title/description/key links, and emits standard markdown format, providing useful behavioral context beyond 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 concise with four sentences, each serving a purpose: general capability, process description, output format, and use cases. Front-loaded with the primary action and no wasted words.

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 no output schema, the description fully explains the output as a single text blob ready for deployment. It covers the extraction elements and format, making the tool's functionality complete for selection and invocation.

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%, so parameters are well-documented there. The description does not add significant new meaning beyond the schema, only implying the extraction of title/description/key links. Baseline 3 is appropriate.

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, resource, and output format. It also distinguishes itself by mentioning AI crawler indexing, which differentiates it from sibling tools like ai_visibility_check or 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?

The description explicitly lists use cases: getting a client's site indexed, drafting for own project, or auditing competitors. It implies when to use but does not explicitly state when not to use or provide alternatives, though the context is clear enough.

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

A3.6/5.0
Disambiguation2/5

Several tools have functionally overlapping purposes, most notably ask_pipeworx and ask_pipeworx_beta, which currently behave identically. The many prediction-market tools are each distinct but still leave boundaries that require careful reading, and the query/research tools (ask_pipeworx, deep_research, potentially validate_claim) have an ease of being confused. Fine verbal descriptions reduce but do not eliminate the ambiguity for an agent.

Naming Consistency3/5

All names are lowercase snake_case and many follow a verb_noun pattern, such as resolve_entity, get_classification, and list_subscriptions. However, the set is inconsistent overall: it mixes single verbs (remember, forget, subscribe), noun-style names (entity_profile, recent_alerts), and several prefixed families (pipeworx_*, polymarket_*). It's readable but not a coherent, uniformly applied convention throughout.

Tool Count2/5

34 tools in one server is well beyond the generally well-scoped 3–15 range. The server appears to bundle several unrelated domains together—WoRMS taxonomy, Pipeworx data research, Polymarket analysis, memory utilities, and subscriptions—creating avoidable cognitive load and making selection more difficult. Splitting into targeted servers would greatly improve the interface.

Completeness2/5

The server is named 'Worms' and includes a WoRMS taxonomy subdomain, but that subdomain only supports searching, classification, and common names—the rest of the marine taxonomy surface is missing (e.g., direct AphiaID record lookup, distributions, synonyms, hierarchical children). The remainder of the tools serve an entirely different data-research purpose, so the server is incomplete relative to its name AND the bundled extra domains add confusion rather than a coherent cohesive coverage.

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