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Idf Events

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

A4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds that it fetches the page, extracts title/description/links, and outputs standard markdown format. This adds useful context but does not disclose potential issues like rate limits or error handling, so 4.

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 two sentences plus a short use-case list, tightly written with no fluff. Front-loaded with the action and output, every sentence earns its place. Excellent structure.

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?

Given the simple tool (2 params, no output schema, good annotations), the description covers functionality, use cases, and output format. Minor gaps: no mention of error handling, prerequisites (internet access), or robots.txt compliance, but still sufficient for the complexity level.

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 well-described in the schema. The main description does not add new meaning beyond the schema (e.g., max_links default is in schema, not description). Baseline is 3, as description adds no additional parameter insight.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates an llms.txt file for a given URL, with specific steps (fetches, extracts, emits). It distinguishes itself from sibling tools like ai_visibility_check by focusing on producing a specific markdown file, but does not explicitly differentiate, so 4.

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 lists concrete use cases: getting a client's site indexed, drafting for own project, auditing competitors. It provides clear context but lacks explicit when-not-to-use or alternative tool references, scoring 4.

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

A3.9/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., three ask_pipeworx variants, multiple Polymarket analysis tools, and several entity-focused tools). Agents may struggle to select the correct tool for tasks like querying data or analyzing prediction markets.

Naming Consistency4/5

All tool names use snake_case, and most follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities). A few names like ai_visibility_check are slightly less conventional, but overall the naming is consistent.

Tool Count3/5

With 32 tools covering a broad range of data services, the count is on the high side but still manageable. However, the server name 'Idf Events' is misleading, as only one tool relates to events in Paris.

Completeness4/5

The tool set covers core workflows for the Pipeworx platform: data querying, research, comparisons, subscriptions, memory, and feedback. Minor gaps exist, but most user needs are addressed.

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