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Epa Regulations

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 provide safety and idempotence hints. The description adds valuable workflow details (fetch, extract, emit) and output format, which are not evident from annotations alone. No contradiction.

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 front-loaded with the core action, followed by a brief workflow and bullet-pointed use cases. Every sentence contributes meaning, 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?

Given the simple tool (2 params, safe, no output schema), the description covers purpose, workflow, output format, and use cases fully. It compensates for lack of output schema by describing the output as a text blob.

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 already described. The description adds no new semantic information beyond reinforcing the URL parameter, so score is at baseline for high coverage.

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 uses a clear verb 'Generate' and specific resource 'production-ready llms.txt file for any URL', distinguishing it from sibling tools. It also lists concrete use cases, leaving no ambiguity about the tool's purpose.

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 clear use cases such as indexing a client's site or auditing a competitor, but does not explicitly state when not to use or compare to alternatives. The context is clear but lacks explicit exclusions.

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

A3.6/5.0
Disambiguation2/5

The server includes many overlapping tools (e.g., multiple ask_pipeworx variants, epa_regulation vs. epa_search vs. discover_tools). More critically, the tool set covers vastly different domains (Polymarket bets, npm packages, AI visibility, memory storage) alongside EPA regulations, making it hard for an agent to distinguish purposes.

Naming Consistency2/5

Tool names use a mix of styles: underscore (epa_regulation, ask_pipeworx), camelCase (deep_research, suggest_questions), and verb phrases (scan_competitor_ai_presence). No consistent pattern is followed across the set.

Tool Count2/5

33 tools is high for a server named 'Epa Regulations'. The vast majority are unrelated to EPA regulations (e.g., Polymarket, npm scanning, memory functions), making the scope mismatched. A focused server should have fewer, domain-specific tools.

Completeness1/5

For a server claiming to be about EPA regulations, only two tools (epa_regulation, epa_search) are directly relevant. The rest are from unrelated domains, leaving severe gaps in expected functionality like rule updates, compliance checks, or cross-referencing with other environmental data.

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