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Twitch

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

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds performance (parallel call), data source fallback details, PatentsView API sunset limitation, and return structure (grouped changes, counts, citation URIs). No contradictions.

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?

Single paragraph packs purpose, examples, data sources, parameter guidance, and sibling distinction. Front-loaded with example queries. Slightly dense but efficient; no wasted words. Could benefit from bullet points but remains clear.

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?

High complexity with multiple sources and fallbacks; no output schema. Description covers purpose, usage context, parameter semantics, behavioral traits (parallel, fallback, soft-fail), and return structure. Complete for an agent to invoke correctly.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for all 3 parameters. The description adds value by providing example values ('7d', '30d', '3m', '1y'), recommended defaults ('Use "30d" or "1m"'), and accepting both ticker and CIK for 'value'. Enhances clarity beyond the schema.

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 starts with example queries, clearly states it's a 'change feed for a company' in a given window, and lists specific data sources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from the sibling tool 'entity_profile' which provides static profiles.

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?

Provides explicit guidance: use entity_profile for static profiles instead. Includes example query patterns and suggests default 'since' values like '30d'. Also describes fallback behavior (GDELT→GNews) and soft-fail for USPTO, helping the agent decide when to use or avoid.

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

A3.7/5.0
Disambiguation2/5

The tool set includes many similar Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded, ask_pipeworx_beta, deep_research) that all serve overlapping purposes, causing confusion. The Twitch-specific tools are distinct but are buried among many unrelated tools.

Naming Consistency3/5

Most tools use snake_case (e.g., get_streams, ask_pipeworx), but there is variation in patterns: some are verb_ noun (get_streams), some are just verbs (remember), and some are adjective_noun (recent_changes). No strong pattern across the whole set.

Tool Count2/5

With 35 tools, the server is bloated for its stated purpose as a 'Twitch' server. Only about 4-5 tools are Twitch-related; the rest are from a data platform (Pipeworx) and generic utilities, making the count feel excessive and unfocused.

Completeness2/5

For a Twitch server, the tool surface is severely incomplete. It lacks essential Twitch features like clips, follows, chat, or channel management. The few Twitch tools present cover only basic stream and user lookup.

Resources