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Dexscreener

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds details about fetching the page, extracting title/description/links, and outputting a markdown blob. However, it doesn't mention potential rate limits or HTTP request behavior.

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?

Description is a single paragraph with front-loaded purpose, followed by technical details and usage examples. Every sentence adds value with no redundancy.

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?

For a simple 2-parameter tool with no output schema, the description fully covers purpose, behavior, and output format ('single text blob'). No missing information.

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 clear descriptions. The description paraphrases the url parameter ('Full URL of the site to summarize') and mentions max_links in context, but adds no new semantic information beyond the schema.

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 verb 'Generate', resource 'llms.txt file', and context 'for any URL'. It distinguishes from sibling tools by specifying a unique capability (generating llms.txt files) not shared by any sibling.

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?

Provides explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing...'. While it doesn't explicitly say when NOT to use it, the examples sufficiently guide appropriate usage.

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
Disambiguation3/5

Several clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve question-answering/research, and the six polymarket_* tools cover closely related prediction-market analysis. Long descriptions help differentiate them, but an agent could easily pick the wrong near-duplicate.

Naming Consistency3/5

All names are snake_case and readable, but there is no consistent verb_noun pattern: some are verbs (search_pairs, validate_claim), some noun phrases (latest_token_profiles, entity_profile), and some prefixes (pipeworx_*, polymarket_*) cover only subsets. The naming is understandable but stylistically mixed.

Tool Count2/5

37 tools is far too many for a server labeled Dexscreener, especially since the majority of tools have nothing to do with DEX data. Even if this is intended as an all-in-one data/research server, the count exceeds what the apparent scope justifies and many tools feel bolted on.

Completeness4/5

For the DEX Screener domain, the core surface is covered: pair lookup, token lookup, search, latest profiles, and boosts. The broader Pipeworx/prediction-market side also has strong coverage with memory, subscriptions, entity resolution, and research tools. Minor gaps exist — some tools feel redundant or exploratory — but there are no critical dead-end workflows.

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