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Arcgis Tucson

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

A4.6/5.0
Behavior5/5

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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior, but the description adds far more: account and paid-plan requirements, parallel decomposition across 5,724 tools, findings-packet shape, gaps[] honesty, contradictions[], citation_uri resolvability, semantic excerpting, and latency expectations. 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 every sentence carries a distinct piece of operational information: auth, alternatives, scope, decomposition model, output guarantees, and timing. It is front-loaded with the most critical constraint (account required) and structured logically, though it reads as a dense paragraph rather than crisp bullets.

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 explains the return packet: verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], hop field, and resolvable citation_uri. It also covers auth, pricing, latency, and sibling routing, leaving no material gap for an agent deciding how to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the schema already explains depth's hop semantics and question flexibility thoroughly. The description echoes this information with examples and latency context, but does not materially add meaning beyond the schema for either parameter.

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 ('research') and a precise resource ('Pipeworx's 1497 STRUCTURED data sources') and explicitly contrasts with open-web search. Clearly distinguishes itself from ask_pipeworx with scope and use-case, so an agent can select the right sibling without opening schemas.

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?

Gives explicit when-to-use guidance for broad/multi-part structured-data questions, and names alternatives with conditions: use ask_pipeworx when not signed in, for single lookups, or for breaking/current-news topics. Includes a concrete second-hop iteration policy tied to depth values.

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
Disambiguation3/5

While individual tool descriptions are detailed and specific, the set includes overlapping tools like ask_pipeworx and ask_pipeworx_grounded, and several prediction market tools with similar purposes (bet_research, polymarket_edges, polymarket_arbitrage). The broad range of unrelated domains means many tools are distinct, but some pairs are ambiguous.

Naming Consistency2/5

Tool names use snake_case but follow no consistent pattern. Some are verb_noun (ask_pipeworx, query_layer), some noun_verb (ai_visibility_check, entity_profile), and some have inconsistent structure (discover_tools, recent_alerts). The mix of conventions reduces predictability.

Tool Count1/5

33 tools is excessive for a server named 'Arcgis Tucson', as only 3 tools relate to ArcGIS (search_datasets, layer_info, query_layer). The rest span completely unrelated domains (Pipeworx data, Polymarket, memory, npm scanning, etc.), creating a severe mismatch between server name and tool functionality.

Completeness3/5

Considering the actual tool surface (a diverse data retrieval and prediction market analysis set), it is reasonably complete for common lookups (SEC, FDA, economics, news, bets). However, it lacks web search and the ArcGIS tools are minimal. The absence of a cohesive domain makes completeness hard to judge, but for the implied data-retrieval purpose, it's passable.

Resources