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disify

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 1500 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 5,743 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?

Annotations already provide readOnly/idempotent/destructive hints, and the description adds substantial context beyond that: account and paid-tier requirements, parallel facet decomposition, findings packet structure, explicit gaps[] instead of invented answers, resolvable citation_uri guarantee, contradictions[] scans, semantic excerpting, and expected latency of 15-60s up to ~90s. No contradiction with 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 description is long but information-dense, front-loading the most actionable fact (account required). Every sentence adds a distinct operational detail — scope, exclusions, output, latency, citation behavior — with minimal filler. A few clauses could be tightened, but the density earns a high score.

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, lack of output schema, and need to choose among siblings, the description is complete: prerequisites, tier restrictions, exact output shape, gap behavior, citation resolution, hop behavior, and runtime expectations are all covered. The example questions further ground the intended use.

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%, so the baseline is 3, but the description adds meaningful semantics: depth values are explained behaviorally ('standard re-angles unanswered gaps', 'thorough chases leads'), and the question parameter is clarified as accepting broad/multi-part natural language that gets decomposed. This goes beyond the schema's simple field 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?

States a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources... in ONE call', and distinguishes itself from open-web search and the ask_pipeworx siblings. The mechanism (decomposes into facets, routes to 5,724 tools in parallel) and return format are clearly described.

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 names alternatives and selection criteria: 'For a single lookup use ask_pipeworx', 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx', and 'If you are not signed in, use ask_pipeworx instead'. Also states its own best-fit: 'Best for broad/multi-part questions over structured data'.

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

A3.6/5.0
Disambiguation2/5

Several tools have nearly identical or heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) all involve finding/analyzing prediction-market opportunities. deep_research and ask_pipeworx also overlap as general query routers, and ai_visibility_check vs scan_competitor_ai_presence is another confusable pair.

Naming Consistency3/5

All names use snake_case and are descriptive, but verb placement is inconsistent: some are verb-first (check_domain, compare_entities, resolve_entity), others are verb-last or noun-like (ai_visibility_check, entity_profile, pipeworx_trending, bet_research). There is no chaotic camelCase mix, but the pattern is not predictable enough to guess a tool's behavior from its name.

Tool Count2/5

33 tools is far above the typical well-scoped range and the set spans multiple unrelated domains (data lookup, prediction markets, AI visibility, memory, subscriptions, email/domain validation) that have no cohesive purpose under the 'disify' name. Most tools are not related to domain or email checking, making the count feel like a grab bag rather than a focused toolkit.

Completeness2/5

For a server named 'disify', the core domain-validation surface is minimal (only check_domain and validate_email) and misses obvious operations like WHOIS lookup or breach/debounce checks. As a general data toolset it is broad but shallow in each area, with gaps such as entity_profile only supporting US public companies and no update/delete operations for most data resources.

Resources