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hackernews

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 readOnly, idempotent, and non-destructive, and the description adds behavior: 'Fetches the page, extracts title/description/key links' and outputs a 'single text blob.' This explains the fetch-and-transform process and output format 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 front-loaded with the primary action, then provides process detail and use cases in a compact format. Every sentence adds value, and the 'Useful for' list is structured for easy scanning.

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 two-parameter generator with robust annotations, the description covers the output ('standard llms.txt markdown format', 'single text blob ready to drop at site-root/llms.txt') and the high-level workflow. No output schema exists, but the description sufficiently communicates the return value.

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 and max_links) with clear descriptions, giving 100% coverage. The tool description doesn't add parameter-specific detail beyond the schema, so the baseline 3 applies.

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 opens with a specific action: 'Generate a production-ready llms.txt file for any URL' and details the process (fetches page, extracts title/description/key links, emits standard markdown). This clearly distinguishes the tool from sibling tools, none of which target llms.txt generation.

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 'Useful for' section explicitly lists three concrete scenarios: getting a client's site indexed, drafting for own project, and auditing competitor presence. It doesn't provide exclusions or alternative tool references, but given the absence of similar siblings, this is clear context.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, and deep_research also overlaps with them. The Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have fuzzy boundaries. An agent could easily pick the wrong meta-tool or duplicate functionality.

Naming Consistency3/5

All names are snake_case, but the pattern is mixed: some are verb_noun (get_item, list_subscriptions, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_edges), some are adjective_noun (deep_research, recent_alerts), and a few are single verbs (forget, recall, remember, subscribe). This is readable but not a consistent convention.

Tool Count2/5

With 36 tools, this is far too many for a server named 'hackernews'. The bulk of the tools concern Pipeworx data research, prediction markets, memory, and subscriptions — unrelated to the server's apparent purpose. Many of these could be split into separate servers, and the HN-specific functionality would be better served by a focused set of ~5-8 tools.

Completeness2/5

For the Hacker News domain implied by the server name, the surface is incomplete: there are read-only tools (search, top stories, item/comments) but no write functionality (submit, comment, vote) and no user profile access. The broader data-research capabilities are fairly comprehensive, but that does not rescue the server's coherence given its stated name.

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