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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?

Beyond annotations (readOnly, idempotent, etc.), description adds that it fetches the page, extracts title/description/key links, and emits standard format. Output is a single text blob, ready to deploy. Fully transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with main purpose. Each sentence adds value. Could be slightly more concise but overall efficient.

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?

With two parameters, rich annotations, and no output schema, the description covers function, output, and use cases. Complete for a simple read‑only tool.

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%, both parameters are described. Description adds no new information beyond what is in the schema (e.g., default 25, max 50 is already in schema). 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?

Clearly states the tool generates an llms.txt file for a given URL, with specific details about extraction and output format. Differentiates from sibling tools by describing its specific function.

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?

Provides explicit use cases (getting a client's site indexed, drafting for own project, auditing competitor). Does not explicitly contrast with siblings or mention when not to use, but context is clear.

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

A3.8/5.0
Disambiguation3/5

Tools are mostly distinct but several ask_pipeworx variants and research tools overlap in purpose, which could lead to agent confusion. The presence of memory and subscription tools adds unrelated functionality.

Naming Consistency2/5

Naming is inconsistent, mixing snake_case with varying verb patterns (ask, get, search, scan, etc.) and no clear convention. Some tools have descriptive phrases (e.g., generate_llms_txt) further breaking consistency.

Tool Count2/5

34 tools is excessive for a coherent server, covering too many disparate domains (genes, data queries, betting, memory) without clear focus. A gene server should have far fewer tools.

Completeness3/5

Gene-related tools are complete for basic queries (search, get, resolve), but the server's main purpose (HGNC) is overshadowed by many unrelated Pipeworx tools, creating a mismatch. The overall surface is broad but lacks domain focus.