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Etherscan

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 declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavioral context: latency expectations (15-90s), account/paid-tier requirement, guaranteed gaps[] that are 'never invented', hop fields, a citation_uri that is only present when fetchable, contradictions[] for standard/thorough, and semantic excerpting instead of head-truncation. 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; virtually every sentence adds actionable detail (account fallback, latency, citation semantics, gaps behavior). It is front-loaded with the most critical constraint (account requirement) and the alternative routing, though it is slightly more verbose than strictly necessary.

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 only 2 parameters and no output schema, the description fully covers the output packet shape (verbatim evidence, confidence, source, fetched_at, citation_uri), latency, gaps[], contradictions[], and the account/plan constraint. Nothing essential for an agent to invoke this tool correctly is missing.

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 baseline is 3. The description goes beyond by giving illustrative question phrasings and explaining the practical effect of depth levels (standard re-angles gaps, thorough chases leads) with user-facing consequences like paid plan requirements, adding marginal value over the schema.

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+resource: 'grounded multi-source research' across Pipeworx's 1500 structured data sources, decomposing questions into facets and routing them in parallel. Explicitly distinguishes itself from open-web search and clarifies the sibling ask_pipeworx's role for single lookups and current news, so an agent can select it correctly.

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 guidance with concrete examples ('compare X and Y's regulatory + financial exposure') and when-not-to-use exclusions ('single lookup use ask_pipeworx', 'BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'). Also names the account prerequisite fallback, leaving no ambiguity about context or alternatives.

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
Disambiguation1/5

Multiple tools serve nearly identical purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with only marginal differences, and the Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) heavily overlaps in its goal of finding betting edges. An agent would struggle to pick the right tool without reading every description in detail.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: verb_noun (get_balance, list_transactions), noun phrases (entity_profile, recent_changes), brand prefixes (pipeworx_trending, polymarket_edges), and adjectival forms (deep_research, compare_entities). The naming is readable but does not follow a single predictable convention.

Tool Count2/5

With 36 tools, the count exceeds the 25+ threshold for 'too many' even for a general-purpose data server. The situation is worsened by the fact that the server is named Etherscan but only 5 of the 36 tools relate to Ethereum/blockchain, making the count unjustified for the apparent purpose.

Completeness4/5

For the de facto domain (Pipeworx data routing, prediction-market research, entity profiles, claim validation, subscriptions, memory), the tool surface is quite comprehensive: it covers lookup, research, comparison, grounding, and monitoring. Missing are a few edge operations (e.g., no Etherscan transaction-by-hash tool), but the broader domain is well-covered with only minor gaps.

Resources