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Entur

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

A4.1/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds behavioral details: it fetches the page, extracts metadata, and outputs standard markdown. It does not contradict annotations and provides useful context beyond them.

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?

The description is a single dense paragraph that effectively communicates purpose, behavior, and use cases. It is concise but could be slightly more structured (e.g., bullet points for use cases).

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 2 parameters, no output schema, and thorough annotations, the description adequately covers the tool's behavior and output (single text blob). It doesn't explain error handling or edge cases, but for a simple read-only tool, this 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 repeats some info (default 25, max 50 for max_links) but adds no additional semantics beyond what the schema already provides. 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 clearly states the tool's purpose: generating a production-ready llms.txt file for a given URL, with specific verbs ('generate', 'fetches', 'extracts', 'emits') and resource ('llms.txt file'). It distinguishes itself from sibling tools like 'scan_competitor_ai_presence' or 'deep_research' by focusing on this specific output format.

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 use cases: '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.' However, it does not explicitly state when not to use the tool or mention alternatives, though none are obvious among siblings.

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

Although many tools are individually well-described, there are several overlapping clusters: three ask_pipeworx variants, multiple polymarket edge/arbitrage tools, and AI-visibility checks vs their competitor-comparison wrapper. An agent can easily pick the wrong one because the boundaries (beta vs stable, grounded vs routed, edge vs arbitrage) are subtle despite the verbose descriptions.

Naming Consistency3/5

The set is consistently snake_case and mostly readable, so naming is not chaotic. However, the pattern is mixed: some tools use entur_/polymarket_/pipeworx_ prefixes, others are bare verbs (remember, recall, forget), and some are noun phrases (entity_profile, pipeworx_trending). The ask_pipeworx family also doesn't follow the pipeworx_ prefix convention used by neighboring tools.

Tool Count2/5

34 tools is well past the healthy range for a focused MCP server, and only three tools relate to the Entur transport domain implied by the server name. The other 31 tools form a separate, broad data/prediction-market product that appears bolted on, making the count inappropriate for the apparent purpose.

Completeness2/5

The Entur transport subset has stops search, departures, and journey planning, but misses common public-transport needs such as disruptions, service alerts, and fare/ticket information. The broader tool set is extensive but lacks a single coherent domain to be complete against, leaving the overall surface scattered and hard to trust as an integrated whole.

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