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Trakt

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

A4.5/5.0
Behavior4/5

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

The description discloses that the tool fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This aligns with annotations (readOnlyHint, idempotentHint) and adds behavioral context beyond the annotations, such as the extraction process and output format.

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 followed by a usage list. The main action is front-loaded, and every sentence earns its place. No redundant information.

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 the simple tool (2 params, no output schema), the description fully explains the output format and usage. Annotations cover safety aspects. The description is complete enough for an agent to understand what the tool does and what to expect.

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?

The input schema provides descriptions for both parameters (url and max_links), so baseline is 3. The description adds value by specifying default max_links (25) and max (50), and clarifying that the URL should be a full URL. Thus, it enhances parameter understanding beyond schema alone.

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 a given URL, specifying the actions (fetch, extract, emit) and the output format. It distinguishes itself from sibling tools by its specific function, 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 concrete examples of when to use the tool (e.g., getting a client's site indexed, drafting for own project, auditing competitors). While it does not explicitly mention when not to use it or alternatives, the usage contexts are clear and helpful.

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
Disambiguation2/5

The tool set has significant overlap among query and research tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research) and among entity/company tools (entity_profile, compare_entities, recent_changes). Despite detailed descriptions, an agent would struggle to select the correct tool without careful reading, especially for nuanced differences.

Naming Consistency2/5

Tool naming is inconsistent: some start with verbs (ask_, generate_, validate_, scan_, subscribe) while others are nouns (entity_profile, popular, trending, search, recent_alerts, recent_changes). The snake_case style is consistent, but the verb_noun pattern is not, making predictions of tool names difficult.

Tool Count3/5

38 tools is on the high side for a single server, but the scope is broad (general query, research, Trakt, subscriptions, memory). The count is appropriate for the wide range of functionality, though some tools could be merged to reduce cognitive load.

Completeness4/5

The tool set covers a very wide range of tasks: querying, research, entity profiles, comparisons, subscriptions, memory, Trakt operations, etc. For the Trakt domain, it has all essential operations (search, get, list, trending). The Pipeworx side has a comprehensive set for data access, grounding, and validation. Minor gaps exist (e.g., no update for subscriptions), but overall it is well-covered.

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