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httpcat

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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent behavior. The description complements these hints by outlining the workflow ('Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format') and the output shape ('single text blob'), adding value without contradicting 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?

Four sentences, front-loaded with primary purpose, then process, output, and use cases. Every sentence carries information, with no filler or 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?

The description covers what, how, output format, and example scenarios. With no output schema, it still clearly conveys the result ('standard llms.txt markdown format'), making it complete for the tool's low complexity.

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?

Input schema covers both parameters (url, max_links) with full descriptions (100% coverage), so the description doesn't need to repeat them. The description adds minimal extra parameter semantics, but the schema already does the heavy lifting.

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 concrete resource ('llms.txt file for any URL'), and names target AI crawlers. It clearly distinguishes itself from sibling tools by focusing on the standardized llms.txt output.

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 by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor'), which makes the intended context clear. It does not mention sibling alternatives or when NOT to use the tool, so it falls short of the highest bar.

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

A3.5/5.0
Disambiguation2/5

Several tools have overlapping or near-identical purposes, e.g., ask_pipeworx and ask_pipeworx_beta are explicitly described as currently equivalent, and polymarket_arbitrage, polymarket_edges, and bet_research all target prediction-market opportunities. Descriptions help somewhat, but boundaries remain fuzzy for agents.

Naming Consistency2/5

Tool names follow no consistent convention: some are verb-first (get_status_cat, remember), others are noun phrases (entity_profile, deep_research), and many use domain prefixes (ask_pipeworx, polymarket_*, pipeworx_feedback). While snake_case is maintained throughout, the structural pattern is mixed and unpredictable.

Tool Count2/5

At 33 tools, the server is well beyond the typical well-scoped range (3-15). The count is inflated by multiple overlapping meta-tools and unrelated utility groups (HTTP cats, Pipeworx research, prediction markets, memory, subscriptions), making the set feel bloated rather than focused.

Completeness2/5

The server name 'httpcat' implies HTTP status-cat functionality, but only 2 of 33 tools serve that purpose, leaving the named domain largely uncovered. As a general-purpose data server, the collection still has gaps (e.g., no direct trading, no general web search) and appears to be a random assortment of features rather than a coherent product.

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