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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

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive, and open-world behavior. The description adds details: fetching the page, extracting title/description/key links, and emitting standard llms.txt markdown format. It also specifies the output is a single text blob ready to drop at site-root/llms.txt, providing clear 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: two sentences plus a bullet list of use cases. It is front-loaded with the core action and output format, with no redundant or unnecessary wording.

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 adequately explains the output (single text blob in standard llms.txt format). The tool is simple with only two parameters, and the description covers purpose, behavior, and use cases comprehensively. There are no gaps for this complexity level.

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?

Schema coverage is 100% with descriptions for both parameters (url, max_links). The description adds context by mentioning the extraction of links (implicitly tied to max_links) and the output format, but does not delve deeply into parameter semantics. Still, it provides useful usage context, so a 4 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 actions: fetches page, extracts title/description/key links, and emits standard markdown. It distinguishes itself from siblings by being the only tool for this specific task.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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 by AI, drafting llms.txt for own project, or auditing competitor's AI crawler view. This provides clear context on when to use the tool, with no competing sibling tools for this purpose.

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
Disambiguation5/5

Each tool has a clear, distinct purpose with detailed descriptions that differentiate overlapping capabilities (e.g., ask_pipeworx vs deep_research vs ask_pipeworx_grounded vs bet_research). No two tools appear redundant; even similar prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have specific scopes.

Naming Consistency5/5

Tool names consistently use lowercase snake_case (e.g., ai_visibility_check, compare_entities, pipeworx_trending, polymarket_kalshi_spread). Single-word exceptions (ephemeris, lookup, observers, recall, remember, vectors) are common short verbs and do not break the pattern. No mixing of camelCase or other conventions.

Tool Count4/5

35 tools is above the typical 3-15 range, but the server is a comprehensive data platform covering multiple domains (SEC, FDA, FRED, prediction markets, memory, subscriptions, feedback). The count is justified given the breadth; it feels slightly heavy but not bloated or redundant.

Completeness5/5

The tool surface covers the full lifecycle for a data/research platform: discovery (discover_tools, suggest_questions), entity resolution (resolve_entity), lookups (ask_pipeworx, entity_profile), comparison (compare_entities), validation (validate_claim), prediction-market operations (polymarket_*), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list_subscriptions/recent_alerts), and meta/feedback (pipeworx_feedback, pipeworx_trending). No obvious gaps for typical workflows.

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