Skip to main content
Glama

Cataas

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, and destructiveHint=false. The description adds value by describing the process (fetches page, extracts content, emits markdown) without contradicting 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 concise (three sentences) and front-loaded with the primary purpose. Every sentence adds value: purpose, process, and use cases. No wasted words.

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 low complexity (2 parameters, no output schema, rich annotations), the description is nearly complete. It explains the output format and typical use, though it omits potential error conditions or URL format specifics, which are minor gaps.

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%, with both parameters documented. The description restates the default and maximum for max_links, which is already in the schema, adding no new semantic meaning beyond the structured fields.

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 specific verb ('Generate') and resource ('llms.txt file') with a clear target ('any URL'). It effectively distinguishes the tool from siblings by focusing on AI crawler indexing, a unique function among the listed sibling tools.

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 (client indexing, personal project, competitor auditing), which clarifies when to use the tool. It lacks explicit when-not-to-use guidance, but the scenarios are concrete and sufficient for most agents.

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

All Cataas 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

Several tools intentionally overlap: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and ask_pipeworx/ask_pipeworx_grounded/deep_research plus discover_tools/suggest_questions sit close together. The long descriptions clarify differences, but an agent still has to choose between near-equivalent entry points.

Naming Consistency3/5

All names are readable snake_case, but there is no single consistent convention: verb-led names like list_tags and resolve_entity sit alongside noun-led names like random_cat, entity_profile, and polymarket_arbitrage. Domain prefixes like polymarket_ and pipeworx_ help, but the mixed grammar makes the surface less predictable.

Tool Count2/5

34 tools is far beyond what a cat-image server needs; only 3 tools relate to Cataas, while the rest form a sprawling Pipeworx data, prediction-market, memory, and subscription suite. The count is in the 'too many' range and most tools are outside the server's apparent stated domain.

Completeness3/5

The cat-image core covers random cats, tag-filtered cats, and tag listing, but omits other Cataas-style operations like fetching by cat ID or creating cat images with text/effects. The embedded Pipeworx side is broad, but it does not fill the gaps in the server's named cat API purpose.

Resources