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Watchmode

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

TDQS

A4.9/5.0
Behavior5/5

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

Discloses multi-source fan-out, fallback logic, soft-fail for PatentsView, and output structure (grouped changes, count, URIs). No contradiction with annotations (`readOnlyHint`, `idempotentHint`); adds important context beyond them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description packs extensive detail into a single paragraph without waste, and front-loads example queries. Slightly less structured than ideal (could use bullet points for multiple sources), but still concise and effective.

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 complexity (multiple external APIs, fallback, soft-fail, multiple output fields) and lack of output schema, the description fully covers what the agent needs: sources, failure modes, return structure, and copy examples.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds meaning: `type` constrained to 'company', `since` explains ISO vs relative formats with examples ('7d', '30d', '1y'), `value` clarifies ticker vs CIK. Every parameter gains clarity.

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 uses specific verbs ('change feed for a company') and clearly distinguishes from sibling tool `entity_profile` by contrasting dynamic vs static profile. Examples of natural language queries make purpose immediately graspable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (`What's new with X`) and when not to (`use entity_profile instead for static profile`). Also explains fallback behavior (GDELT→GNews) and soft-fail for patents, guiding correct invocation.

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