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Google_sheets

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?

Discloses far more than the annotations alone: account/plan requirements, latency, gaps[] never invented, citations always fetchable, contradiction detection, semantic excerpting, and the key limitation that un-cataloged topics return empty gaps. All of this aligns with the readOnly, openWorld, and idempotent annotations rather than contradicting them.

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?

Dense but organized, with the critical account requirement front-loaded, followed by core capability, use cases, exclusions, and behavioral details. While long, every sentence carries information and the flow is logical; it's a heavy read but earnes its length.

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 there is no output schema, the description fully specifies the return packet (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[]) and covers latency, plan tiers, and semantically excerpted records. It addresses all relevant sibling alternatives and edge cases (breaking news, unsigned users, un-cataloged topics).

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 documents both parameters thoroughly (100% coverage), which sets the baseline at 3. The description adds real value by noting that depth:"thorough" requires a paid plan and by clarifying that broad/multi-part questions are exactly what question is designed for, reinforcing the depth semantics.

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 begins with a concrete verb and resource: grounded multi-source research across Pipeworx's 1500 structured data sources, with explicit details on decomposition and parallel routing to 5,743 tools. It clearly distinguishes itself from open-web search and from ask_pipeworx, so an agent can tell these apart immediately.

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?

Provides explicit when-to-use and when-not-to-use guidance: best for broad/multi-part structured-data questions, while single lookups, breaking news, colloquial current events, and signed-out users are directed to ask_pipeworx. It even names the alternative tool and the condition for choosing it.

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

B3.1/5.0
Disambiguation2/5

Several tools are nearly identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same, and ask_pipeworx, ask_pipeworx_grounded, and deep_research all serve overlapping research/query purposes. discover_tools and suggest_questions both act as discovery entry points, while the five polymarket tools differentiate primarily through intricate details that are easy to confuse.

Naming Consistency2/5

Naming conventions are mixed across the set: verb_noun (ask_pipeworx, resolve_entity, validate_claim), noun_verb (sheets_append, sheets_create), noun_noun (polymarket_arbitrage, entity_profile), and bare verbs (forget, recall, subscribe). The sheets tools are internally consistent but the broader collection has no uniform pattern, with awkward names like pipeworx_trending and search_within.

Tool Count2/5

With 36 tools, the server exceeds the 25-tool threshold for 'too many'. The Google Sheets portion accounts for only 5 tools, while the majority are highly specialized Pipeworx and Polymarket tools that could be consolidated or dramatically reduced. The count feels inflated relative to the advertised 'Google_sheets' server name and its actual core purpose.

Completeness2/5

For a server named Google_sheets, the essential operations exist (create, read, write, append, list structure), but there is no delete/clear range tool and no way to add a new sheet to an existing spreadsheet. For the broader data/research scope, important lifecycles are missing (e.g., no direct way to write Pipeworx results into Sheets, no update for subscriptions, only cancel). The set falls short of fully covering either domain.

Resources