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Entso E

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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context (fetches page, extracts key elements, emits markdown format) beyond what annotations provide, 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 concise: a single paragraph with four sentences. It front-loads the main purpose, then details, then use cases. No superfluous words or 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?

Given the simplicity (2 params, no output schema), the description sufficiently covers what the tool does and its output. It lacks details on error handling (e.g., unreachable URL) but is otherwise complete for a straightforward 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%, so baseline is 3. The description reiterates the parameters (url, max_links) but does not add significant new meaning beyond the schema descriptions. No additional validation or format details are provided.

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 an llms.txt file for a URL, with specific actions (fetches page, extracts title/description/key links) and output format. It distinguishes from siblings by listing explicit use cases (getting client site indexed, drafting own project, auditing competitor).

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 explicit use cases via 'Useful for:' but does not explicitly state when not to use the tool or mention alternative tools. However, the listed use cases give clear guidance on appropriate contexts.

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

B3.3/5.0
Disambiguation2/5

The ENTSO-E energy tools are clearly distinct, but the Pipeworx half contains overlapping query modes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx/ask_pipeworx_grounded/deep_research/validate_claim all route natural-language questions to the same underlying catalog. The Polymarket tools also blur edge detection, arbitrage, fill-risk, and persistence tracking, so an agent can easily select the wrong one.

Naming Consistency2/5

The five ENTSO-E tools use a clean snake_case noun pattern, but the rest mix brand verbs (ask_pipeworx, bet_research), bare memory verbs (remember, recall, forget), and polymorphic prefixes (polymarket_*), with inconsistent suffixes like beta, grounded, and kalshi_spread. There is no server-wide predictable naming convention.

Tool Count2/5

36 tools is too many for the apparent scope, and the server name promises ENTSO-E while only 5 of 36 tools serve that domain. Even viewed as a general data utility, the count is heavy and includes duplicate query modes, though individual clusters do have some purpose.

Completeness3/5

For an ENTSO-E server, the five energy tools cover the basics (generation, load, price, flow, capacity) but omit common datasets like generation forecasts, balancing/imbalance prices, and outages. The unrelated Pipeworx tools add broad research, memory, and subscription coverage, but the overall surface feels like a general-purpose assistant with an energy add-on rather than a complete energy domain.

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