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Bhagavad Gita

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

A4.6/5.0
Behavior5/5

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

Even though annotations already mark the tool readOnly/idempotent, the description adds substantial context: account requirements and paid depth tier, expected latency, parallel decomposition, gaps[] semantics, contradictions[], hop field, citation URI fetchability, and semantic excerpting. Nothing 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.

Conciseness2/5

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

The core guidance is front-loaded and the content is genuinely useful, but a large block covering structured sources, findings packets, and ask_pipeworx guidance is repeated verbatim multiple times. This creates significant redundancy and makes the description harder to scan than it should be.

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 complex tool with no output schema, the description thoroughly covers the output packet, citation format, gaps and contradictions behavior, depth differences, latency, authentication requirements, and exclusions. The minor ambiguity around per-finding citations versus citation_uri presence does not prevent an agent from using 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?

Schema coverage is 100%, so the schema already documents both parameters well. The description adds extra meaning by tying depth to pricing ('thorough needs a paid plan'), gap-recovery behavior, contradictions scanning, and latency expectations, which is useful beyond the enum 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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources', with decomposition into facets and parallel routing to 5,743 tools. It explicitly contrasts itself with open-web search and with ask_pipeworx, 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?

It gives explicit when-to-use guidance ('Best for broad/multi-part questions over structured data') and when-not-to-use guidance ('If you are not signed in, use ask_pipeworx instead', 'For a single lookup use ask_pipeworx', and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'). It names the alternative and explains why the alternative is better in those cases.

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
Disambiguation4/5

Individual tools have detailed descriptions and mostly distinct purposes, but the mix of domains (scripture, finance, data lookup) could confuse an agent about which tool to choose for a given task. However, within each subdomain, tools are clearly differentiated.

Naming Consistency2/5

Tool names follow no consistent pattern: some are verb_noun (ask_pipeworx, compare_entities), some are noun_verb (entity_profile, recent_changes), and some are single verbs (forget, recall). The mix of snake_case with varied starting parts makes naming unpredictable.

Tool Count1/5

With 23 tools but only 3 related to the server's stated purpose (Bhagavad Gita), the tool count is severely mismatched. The vast majority belong to data analytics and finance, making the set feel bloated and mis-scoped.

Completeness1/5

For a Bhagavad Gita server, only list_chapters, get_chapter, and get_verse are provided, missing obvious features like search, commentary comparison, or multiple translations. The other tools are irrelevant to the domain, leaving it extremely incomplete.

Resources