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Etherscan

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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds context by stating it fetches the page and extracts content, which is consistent with these hints. No contradiction; missing details like rate limits are acceptable given the annotations.

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 comprised of three efficient sentences, front-loaded with the core purpose. Every sentence provides necessary context with no extraneous wording.

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 annotations covering safety and idempotence, and the description explaining the use cases and output format (a single text blob), the tool is well-specified. The only minor gap is no explicit mention of error handling or edge cases, but overall it's sufficiently complete.

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 the baseline is 3. The description does not add new information beyond the schema for parameters; it only briefly mentions max_links in context. No extra value provided beyond what the schema already offers.

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 it generates a production-ready llms.txt file for any URL, explaining the process and output format. It distinguishes the tool from siblings by focusing on AI crawler indexing, which no sibling tool directly addresses.

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: getting a client's site indexed, drafting for own project, or auditing competitors. It does not explicitly state when not to use, but the context is clear enough for appropriate tool selection.

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
Disambiguation1/5

Multiple tools serve nearly identical purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with only marginal differences, and the Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) heavily overlaps in its goal of finding betting edges. An agent would struggle to pick the right tool without reading every description in detail.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: verb_noun (get_balance, list_transactions), noun phrases (entity_profile, recent_changes), brand prefixes (pipeworx_trending, polymarket_edges), and adjectival forms (deep_research, compare_entities). The naming is readable but does not follow a single predictable convention.

Tool Count2/5

With 36 tools, the count exceeds the 25+ threshold for 'too many' even for a general-purpose data server. The situation is worsened by the fact that the server is named Etherscan but only 5 of the 36 tools relate to Ethereum/blockchain, making the count unjustified for the apparent purpose.

Completeness4/5

For the de facto domain (Pipeworx data routing, prediction-market research, entity profiles, claim validation, subscriptions, memory), the tool surface is quite comprehensive: it covers lookup, research, comparison, grounding, and monitoring. Missing are a few edge operations (e.g., no Etherscan transaction-by-hash tool), but the broader domain is well-covered with only minor gaps.

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