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Watchmode

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 readOnlyHint, idempotentHint, and openWorldHint, so safety is clear. The description adds process details (fetches page, extracts elements, outputs markdown) and output format, which goes beyond annotations without 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?

Four sentences with no fluff: purpose, process, output, use cases. Front-loaded with key action, each sentence adds value.

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 params, no output schema), the description covers all needed context: what it does, how it works, what it produces, and when to use it. No 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 coverage is 100% and adequately describes both parameters. Description adds no new semantic meaning beyond restating the default/max for max_links. Baseline score of 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?

Clearly states the verb 'generate', resource 'llms.txt file', and scope 'for any URL'. Provides specific use cases that differentiate it from siblings 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?

Lists three explicit use cases (client's site, own project, competitor audit), giving clear context for when to use. Lacks explicit when-not-to-use or alternative tools, but the examples are informative.

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

A3.8/5.0
Disambiguation2/5

The server packs in three near-identical question-answering entry points (ask_pipeworx, ask_pipeworx_beta which explicitly states it 'currently matches ask_pipeworx exactly', and ask_pipeworx_grounded), plus overlapping research tools like deep_research and validate_claim — an agent can easily misroute. The Watchmode cluster also blurs title_search vs list_titles and list_titles vs releases. The very detailed descriptions save it from a 1, but the ask_pipeworx_beta duplicate is a genuine selection hazard.

Naming Consistency4/5

Everything is snake_case and the clusters follow good prefixes — title_detail/title_search/title_seasons/title_sources, polymarket_edges/polymarket_arbitrage/polymarket_fill_risk, ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded. Minor inconsistency: scan_competitor_ai_presence and ai_visibility_check are sibling tools but don't share a naming pattern, and the pipeworx_*/ask_pipeworx*/plain-noun (genres, sources, regions) mix is slightly uneven. Still readable and mostly predictable.

Tool Count2/5

41 tools is heavy, but the real problem is scope: only ~10 of them are Watchmode streaming tools, while the rest are a Pipeworx data-router suite, a Polymarket/Kalshi prediction-market suite, memory, subscriptions, npm scanning, and llms.txt generation. This isn't a focused Watchmode server — it's three or four unrelated product surfaces bolted together under one name. Any single coherent feature area would justify closer to 10-15 tools.

Completeness3/5

The Watchmode core is actually well covered for a read-only catalog: search, detail, seasons/episodes, source availability, releases, and directory tools (genres/regions/networks/sources) make a complete browse-to-detail flow. But the overall surface is unfocused — AI visibility, npm deps, and llms.txt have nothing to do with the apparent purpose — and several tools are gated (deep_research needs an account, ask_pipeworx_grounded costs extra, ai_visibility_check needs a BYO key), leaving dead ends for anonymous agents.

Resources