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Twitch

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1499 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,738 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, and the description adds substantial non-obvious behavior beyond them: never-invented gaps[], contradiction scans, latency expectations, citation_uri fetchability guarantees, semantic excerpting of large records, and hop-based iteration. The 'NOT open-web search' claim clarifies, rather than contradicts, the openWorldHint annotation.

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

Conciseness3/5

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

The description is front-loaded and information-dense, but it is quite long and somewhat redundant: contradictions[] are mentioned both in the main narrative and again in the depth explanation, and citation_uri details appear twice. A more structured layout with fewer repeated points would improve scannability without losing content.

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?

Despite having no output schema, the description fully explains the return packet: verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop semantics. It covers latency, prerequisites, depth modes, and fallback routing, so an agent has everything needed to invoke the tool 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?

The schema already covers both parameters (100% coverage), but the description adds meaning beyond the raw enum: it flags that depth:'thorough' requires a paid plan and explains the behavioral difference between quick, standard, and thorough hops. This is genuinely useful enrichment beyond the schema descriptions.

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 names a specific verb ('research') attached to a clearly defined resource: Pipeworx's structured data sources. It explicitly contrasts itself with open-web search and distinguishes from sibling tools like ask_pipeworx, so an agent can identify what this tool is and is not.

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?

The description gives explicit when-to-use guidance: best for broad/multi-part questions over structured data, single lookups should use ask_pipeworx, and breaking/current-news topics should also prefer ask_pipeworx. It also states an account prerequisite and paid-tier requirement, leaving no ambiguity about readiness.

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