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Arcgis Branson

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 1499 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 5,738 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?

Even with annotations declaring readOnlyHint and idempotentHint, the description adds substantial behavioral detail: account and paid-plan requirements, parallel tool routing, findings packet composition, no invented answers via gaps[], second-hop recovery, contradictions[], excerpting behavior, and expected latency. This far exceeds what annotations alone convey.

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 organized and front-loaded with the most crucial operational requirements: account sign-in and the alternative to use if not signed in. Each subsequent block adds necessary behavioral detail; there is minor redundancy around the paid 'thorough' requirement, but complexity justifies the 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?

With no output schema, the description fully covers what the agent should expect: a findings packet with verbatim evidence, confidence, source, fetched_at, a stable pipeworx:// citation, explicit gaps[], contradictions[], hop field, and fetchable citation_uri. It also handles prerequisites, latency, and exclusions, making the tool self-contained.

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 enriches both parameters: 'question' is clarified as broad/multi-part natural language, and 'depth' is tied to facet count, hop behavior, paid gating, and contradiction scans. This adds meaning beyond the raw enum values and type strings.

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 1497 STRUCTURED data sources... in ONE call.' It also differentiates itself from open-web search and asks_pipeworx by describing decomposition, parallel routing, and the returned findings packet.

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 explicitly says 'Best for broad/multi-part questions over structured data' and gives clear alternatives: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx.' It also notes the signed-in requirement and the paid tier for 'thorough', leaving no ambiguity about when this tool should be selected.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Arcgis Branson toolsInspect
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TDQS

A3.9/5.0
Disambiguation5/5

All 33 tools have clearly distinct purposes, even those that seem related like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by use case and behavior. Memory tools (remember/recall/forget) and subscription tools (subscribe/unsubscribe/list_subscriptions/recent_alerts) are similarly distinct.

Naming Consistency3/5

All tools use snake_case, but naming patterns are mixed: some are single verbs (forget, recall), some verb_noun (query_layer, search_datasets), some noun_noun (entity_profile, layer_info), and some longer phrases (polymarket_kalshi_spread, scan_competitor_ai_presence). While readable, the inconsistency makes the set feel less coherent.

Tool Count2/5

With 33 tools, the surface is overly broad for a server named after a specific ArcGIS dataset. Many tools are unrelated to the core purpose (e.g., polymarket tools, npm scanning, AI visibility), making the count feel bloated and unfocused.

Completeness2/5

For the stated ArcGIS Branson focus, only 3 tools (search_datasets, query_layer, layer_info) are relevant, offering only read access. The rest are a miscellaneous collection from the Pipeworx ecosystem and other domains, leaving obvious gaps in GIS functionality and no write or analysis capabilities.

Resources