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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, open-world, idempotent, and non-destructive behavior. The description adds that it fetches the page, extracts title/description/key links, and outputs standard markdown. It also says the output is a single text blob for site-root/llms.txt, which is useful 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: two sentences plus a list of use cases. Every sentence adds value, and the main action is front-loaded. No redundant information.

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 has only 2 parameters, full schema coverage, and rich annotations, the description adequately explains the output format and what is extracted. No gaps remain for the agent to understand the tool's behavior.

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% with both parameters documented. The description does not add significant meaning beyond the schema; it mentions URL and max_links but the schema already includes defaults and limits. 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 verb 'Generate', the resource 'llms.txt file', and the scope 'for any URL'. It explains the process (fetch, extract, emit) and distinguishes itself from sibling tools which have different purposes.

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 three use cases: getting a client's site indexed, drafting llms.txt for own project, or auditing competitor. It does not mention when not to use or alternatives, but the use cases are clear and relevant.

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

A4/5.0
Disambiguation3/5

Tools are richly described with clear use-cases, but several clusters overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research are all question-routing tools with similar names, and the five polymarket_* tools cover adjacent prediction-market analysis. An agent could easily pick the wrong one without reading the full descriptions.

Naming Consistency4/5

All names are snake_case and mostly follow a verb_noun or domain_noun pattern (compare_entities, resolve_entity, denver_query, polymarket_edges, pipeworx_trending). There are minor deviations like single verbs (remember, forget), adjective-first names (recent_alerts, deep_research), and domain-prefixed groups, but the overall style is predictable and readable.

Tool Count2/5

34 tools is a large surface for one server, pushing beyond the 25+ threshold where agents struggle to choose. While the platform is genuinely multi-domain (data lookup, prediction markets, Denver open data, memory, subscriptions, npm checks, llms.txt generation), several niche clusters like the 5-tool Polymarket suite and 3-tool memory trio inflate the count.

Completeness4/5

The server covers its domain well: general querying, grounded verification, deep research, entity resolution, company profiling, change feeds, subscription lifecycle (subscribe/list/unsubscribe/alerts), memory lifecycle (remember/recall/forget), and discovery (discover_tools, suggest_questions). Minor gaps exist—such as no direct browsing of all Pipeworx sources—but agents can work around them.

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