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Devto

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

A4.9/5.0
Behavior5/5

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

Even with readOnly/openWorld/idempotent annotations, the description adds account/paywall requirements, latency bounds, parallel decomposition, gap[]/contradictions[] behavior, citation_uri fetchability, and semantic excerpting. No described behavior contradicts the 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 dense but every sentence carries operational information; the critical prerequisite (account and paid tier) is front-loaded, followed by purpose, routing, and behavioral details. For a tool of this complexity it is appropriately sized with no filler.

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?

There is no output schema, so the description shoulders the burden of explaining the return packet, and it does so in detail (evidence, confidence, source, fetched_at, citations, gaps, contradictions, hop, excerpting). It also covers auth, latency, alternatives, and failure mode for news topics — an agent has enough to select and invoke it 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 already covers 100% of parameters, including enum semantics for quick/standard/thorough, so baseline is 3. The description adds value by explaining the practical consequences of depth choices (default, gap recovery, contradictions, paid tier, ~90s latency) and that question can be broad natural language.

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 names a specific verb ('research') and a concrete resource ('Pipeworx's 1497 STRUCTURED data sources'), and immediately separates itself from open-web search and from ask_pipeworx. It states what it returns (findings packet with evidence/gaps) and for whom it's best (broad/multi-part questions), so an agent can distinguish it from siblings.

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?

Gives explicit routing rules: use ask_pipeworx when not signed in, for single lookups, and for breaking/colloquial current news; use deep_research for broad multi-part structured questions. This is far beyond implied usage and names alternatives with conditions.

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
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but some overlap exists, e.g., ask_pipeworx vs ask_pipeworx_grounded, and several entity/company tools that could be confused. Overall, well-described and mostly disambiguated.

Naming Consistency5/5

Tool names follow consistent patterns: snake_case, with clear prefixes for each group (e.g., polymarket_*, pipeworx_*, get_article, etc.). Naming is predictable and systematic, making it easy to understand the tool's domain and action.

Tool Count2/5

33 tools is high for a server named 'Devto', where only a few tools actually relate to DEV.to. The majority are for Pipeworx data and Polymarket, which are unrelated. The tool count feels bloated and misaligned with the server's apparent primary purpose.

Completeness2/5

For the DEV.to domain, coverage is incomplete: lacks create/update/delete for articles and missing user profile management. The Pipeworx and Polymarket tools are extensive, but that doesn't compensate for the gaps in the core feature set implied by the server name.

Resources