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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

A4.4/5.0
Behavior5/5

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds process details: fetches, extracts title/description/key links, emits standard format. No contradictions; it enriches the agent's understanding of behavior.

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?

Three sentences with front-loaded purpose and output format. No redundant phrases; every sentence adds value. Efficient and clear.

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?

With rich annotations and clear description of process and output, the description is mostly complete. However, it does not mention error handling (e.g., invalid URLs) or limitations, but given the tool's simplicity and annotation coverage, it is sufficient.

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%, with both parameters described. The description adds context about extraction and output format but doesn't provide additional parameter-specific semantics beyond what the schema already says. Baseline score of 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 explicitly states the tool generates a production-ready llms.txt file for any URL, targeting AI crawlers. It specifies the action, resource, and purpose, and distinguishes itself from sibling tools by focusing on llms.txt generation, which is unique among the listed siblings.

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 clear use cases: getting a client's site indexed, drafting for own project, or auditing competitor's AI visibility. It lacks explicit when-not-to-use or alternatives, but the scenarios are well-defined and contextual.

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

While many tools target distinct resources, there is notable overlap between ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and between discovery tools (discover_tools vs suggest_questions). The Polymarket prediction tools also have blurred boundaries (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage). This overlap can confuse an agent trying to select the right tool.

Naming Consistency4/5

Tool names consistently use snake_case and are mostly descriptive. However, naming patterns vary: some start with verbs (list_subscriptions, validate_claim), others with nouns (ask_pipeworx, bet_research). The memory tools use single-word imperatives (remember, recall, forget), which differ from the multi-word pattern. Overall, it's readable but not rigidly consistent.

Tool Count3/5

34 tools is on the high side for a single server, but the breadth of domains (data access, prediction markets, biotech, subscriptions) justifies the count. Some tools feel peripheral (gene_annotations, generate_llms_txt) and could be split off, making the set slightly bloated for the core purpose of business/financial data and prediction markets.

Completeness4/5

The tool surface covers a wide range of data sources (SEC, FDA, FRED, patents, news) and prediction market analysis comprehensively. However, there are minor gaps: no direct web search tool, no tool for fetching a specific SEC filing by accession (though ask_pipeworx may cover it). The set supports most core workflows without dead ends.

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