Skip to main content
Glama

Faa 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 indicate read-only, idempotent, non-destructive behavior. Description adds transparency by detailing the fetch-and-extract process and specifying the output format (standard llms.txt markdown). It does not contradict 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?

Description is two sentences plus a short list, all front-loaded with the core purpose. Every sentence adds distinct value without redundancy. Ideal length for a simple tool.

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 tool's simplicity (2 parameters, no output schema, strong annotations), the description fully covers what it does, how it works, output format, and use cases. No gaps for effective agent usage.

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 schema already documents both parameters thoroughly. The description restates max_links default/max values but adds no new semantic meaning beyond what the schema 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?

Description clearly states the tool generates a production-ready llms.txt file for any URL to help AI crawlers index a site. It specifies the concrete actions (fetch page, extract title/description/key links, emit markdown). This distinct purpose differentiates it from sibling tools like scan_competitor_ai_presence.

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?

Description lists three explicit use cases (client site indexing, own project drafting, competitor auditing), providing clear context for when to use. It does not state when not to use or mention alternatives, but the use cases are specific enough to guide appropriate invocation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Faa Regulations toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,714-tool catalog with heavily overlapping purposes, and ask_pipeworx_beta is currently identical to ask_pipeworx. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all target prediction-market analysis and could easily be confused by an agent. The two FAA tools (faa_regulation, faa_search) are distinct, but they are buried among a dozen unrelated data-lookup and memory tools.

Naming Consistency4/5

Most tools follow a consistent lowercase snake_case verb_noun or noun_verb pattern (faa_search, resolve_entity, compare_entities, validate_claim, discover_tools, unsubscribe). Minor deviations exist, such as ask_pipeworx and pipeworx_feedback lacking underscores, and the polymarket_* family mixes noun-led names, but overall the naming is readable and predictable.

Tool Count1/5

33 tools for a server named 'Faa Regulations' is a severe mismatch: only 2 of the 33 tools (faa_regulation, faa_search) relate to FAA regulations, with the rest covering general data lookups, prediction markets, SEC filings, memory storage, npm dependency checking, and llms.txt generation. The count is far too high for the stated domain, and most tools do not belong in this server at all.

Completeness2/5

The actual FAA surface is thin: faa_search provides keyword lookup and faa_regulation returns full text or a part's section list, so basic citation-lookup workflows work, but there is no update/amendment tracking, no browse-by-part navigation beyond a section list, and no related aviation data such as NOTAMs or TFRs. The dominant Pipeworx tool family is unrelated to FAA regulations, so an agent using this server for its apparent purpose would hit dead ends quickly.

Resources