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dogsapi

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 1517 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 5,798 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."New value: +"How 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)."
  2. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."
  3. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."
  4. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans)."New value: +"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."
  5. Added

TDQS

A4.5/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint=true, openWorldHint=true, idempotentHint=true), the description adds significant behavioral context: it never invents answers, returns explicit gaps[], includes confidence/source/fetched_at/citation per finding, performs semantic excerpting rather than head-truncation, and discloses latency (15-60s, up to ~90s for thorough). It also reveals contradictions[] behavior for standard/thorough. This is strong disclosure beyond what annotations provide.

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 dense and information-rich, front-loading the critical account requirement. However, it's quite long and contains some redundant phrasing that could be tightened. The parenthetical parenthetical 'as ai_visibility_check' at the end is garbled, indicating some editing issues. It earns its length mostly, but could be more structured.

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?

For a tool with no output schema, the description does an excellent job explaining what the findings packet contains: verbatim evidence, confidence, source, fetched_at, stable citation URI, gaps[] and contradictions[]. It also covers important operational details like latency, account requirements, and how large records are handled. An agent has enough to decide when to invoke it and what to expect back.

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 at 100%, but the description enriches the depth parameter by explaining the behavioral difference between quick/standard/thorough (single hop, gap-recovery, contradictions scan, paid tier). The question parameter is adequately described in the schema, and the description adds context about broad/multi-part questions being appropriate.

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 clearly states the tool's purpose: grounded multi-source research across 1517 structured data sources in one call, decomposing questions into facets and routing them in parallel. It explicitly distinguishes itself from open-web search and names the specific alternative (ask_pipeworx) for users not signed in.

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 and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx instead'. It also mentions the account requirement and tier restrictions ('thorough' needs paid plan), which helps an agent route appropriately.

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

Several tools have genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ask_pipeworx_grounded shares the same router, and the polymarket_edges/arbitrage/fill_risk/bet_research group overlaps heavily in purpose. The remaining clusters (dog data, memory, subscriptions) are mostly distinct, so the confusion is concentrated in a few spots but severe there.

Naming Consistency3/5

Nearly all names are lower_snake_case and readable, but the conventions are mixed: get_/list_/ask_/scan_ verb-noun names sit alongside bare verbs (remember, forget, recall), noun-phrase names (entity_profile, bet_research, pipeworx_trending), and a versioned suffix (ask_pipeworx_beta). No single predictable pattern covers the whole set.

Tool Count2/5

At 35 tools, the server is well past the 25+ threshold for feeling bloated, and the count is dominated by unrelated Pipeworx, prediction-market, and AI-visibility tools rather than the dog-data domain implied by 'dogsapi'. Only four tools actually serve the dog API, making the surface both oversized and misaligned with the server name.

Completeness3/5

The broad data-access side is thorough, covering discovery, universal routing, grounded answers, deep research, entity profiles, comparisons, claim validation, memory, and subscriptions. However, the nominal dog domain is thin (list/get/groups/facts with no filtering or additional operations), subscriptions have no update path, and some one-off tools like generate_llms_txt and scan_dependency exist without any surrounding lifecycle.

Resources