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Data Delaware

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 1497 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 5,724 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

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses an account requirement, a paid tier for 'thorough', latency expectations (15-60s, up to ~90s), non-invention via explicit gaps[], contradictions[] for certain depths, semantic excerpting of large records, and the resolvability guarantee for citation_uri. Nothing in the description contradicts the provided annotations.

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 text is long but each sentence carries decision-relevant information—auth prerequisite, sibling routing, scope, exclusions, depth semantics, citation guarantees, and latency. It is front-loaded with the account requirement and when-not-to-use guidance. A single dense paragraph is slightly less scannable than bullets, but there is no redundancy.

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?

With no output schema, the description fully compensates by specifying the findings packet fields (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, hop, citation_uri, gaps[], contradictions[]) and practical behavior. Combined with the annotations and full schema coverage, an agent has enough to select and 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 input schema already documents both parameters thoroughly (100% coverage), so the baseline is 3. The description adds practical meaning beyond the schema: 'thorough' requires a paid plan, 'standard' re-angles gaps while 'thorough' chases leads, and latency expectations are tied to the depth choice. This is meaningful reinforcement rather than duplication.

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 action ('grounded multi-source research... in ONE call'), clearly identifies the resource (1497 structured data sources), and explicitly distinguishes itself from open-web search and ask_pipeworx. It also states the return shape—a findings packet with evidence, confidence, source, fetched_at, citations, and gaps—so an agent knows exactly what this tool does.

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?

It gives explicit when-to-use guidance ('Best for broad/multi-part questions over structured data') and explicit alternatives: single lookups should use ask_pipeworx, and breaking/current-news topics should prefer ask_pipeworx. It even handles the signed-out case by routing to ask_pipeworx, leaving no ambiguity about when to choose this tool versus its siblings.

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

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route queries to the same data sources with only subtle differences. Similarly, bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_edge_tracker all analyze prediction markets, making it hard for an agent to pick the right one without deep reading of descriptions.

Naming Consistency3/5

Tool names follow a mix of patterns: some are verb_noun (generate_llms_txt, list_subscriptions), some noun_verb (ai_visibility_check, bet_research), and some are just nouns (datasets, metadata). The ask_pipeworx family has consistent prefixes but suffixes vary. Overall readable but inconsistent.

Tool Count2/5

34 tools is excessive for a coherent server. The server tries to be a Swiss Army knife covering data lookup, prediction markets, Delaware open data, memory, subscriptions, and misc tools like generate_llms_txt and scan_dependency. Many tools feel tacked on, and the count makes it unwieldy for an agent to navigate.

Completeness3/5

The server covers multiple domains thoroughly (data query via Pipeworx variants, prediction markets with arbitrage and edges, Delaware open data, memory, subscriptions). However, there are gaps: no tool for managing custom pipelines or for updating data. For the broad scope, it is decent but not fully comprehensive.

Resources