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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

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only/idempotent behavior. The description adds meaningful process details—it fetches the page, extracts specific elements, and outputs a standard markdown blob. This provides transparency beyond the annotations without contradicting them.

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 appropriately sized, front-loaded with the primary action, and includes a well-organized list of use cases. Every sentence contributes meaningful information without redundancy.

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 no output schema, the description adequately explains the output format ('single text blob', 'standard llms.txt markdown'). It covers the key behavioral aspects and use cases. It could mention error handling or network dependencies, but these are minor omissions given the annotations.

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%, so parameters are well-documented. The description adds minor context (e.g., url is the site to summarize, output is a text blob) but does not significantly extend the schema's parameter details. This is the baseline 3 for high schema coverage.

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's function with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL'. It also provides a concise description of the process (fetches page, extracts metadata, emits markdown) and distinguishes itself from sibling tools by mentioning competitor auditing use cases.

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 'Useful for' section provides concrete scenarios (client indexing, own project, auditing competitors). It doesn't explicitly name alternative tools, but the context is clear enough for an agent to decide when to use this tool versus related ones.

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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical router variants, and entity_profile/compare_entities/recent_changes/ai_visibility_check all inspect companies from overlapping angles. The six Polymarket tools form a tightly-overlapping mini-domain that further crowds the surface.

Naming Consistency3/5

All names are snake_case, but verbs are inconsistently used: many tools are noun phrases (citation_count, entity_profile, polymarket_edges) while others start with verbs (ask_pipeworx, compare_entities, validate_claim). Some prefixes like ask_* and polymarket_* help, but the overall verb/noun pattern is not coherent.

Tool Count2/5

37 tools is well above the typical 3–15 tool scope, and several are redundant variants (three ask_pipeworx modes) or hyper-specific sub-tools (six Polymarket tools). The server name suggests a focused citation service, but only six tools actually address citations, leaving the set bloated with unrelated data query and memory utilities.

Completeness3/5

For a general data-query gateway, the surface is quite broad and covers lookup, profiling, comparisons, subscriptions, and memory. But as an OpenCitations server it lacks a way to discover papers by topic and the breadth of the other domains is unwieldy and unowned—so notable gaps exist in any plausible stated purpose.

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