Skip to main content
Glama

Arcgis Phoenix

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?

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds substantial context: account/plan requirements, parallel routing to 5,724 tools, gaps[] never invented, resolvable citation_uri, contradictions[], semantic excerpting, and latency expectations. The openWorldHint is consistent with external structured data sources even though this is not open-web search, so there is no contradiction.

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 dense and front-loaded with the account prerequisite and fallback before the method explanation. Each clause adds operational value, though some repetition around ask_pipeworx alternatives could be trimmed.

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 is exceptionally complete: it explains the return packet structure, gaps[], contradictions[], citation resolution, excerpting behavior, latency, authentication, and when to choose alternatives. An agent has enough information to select and invoke 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?

The schema already covers both parameters at 100% coverage with detailed enum meanings. The description adds operational nuance beyond the schema: depth:'standard' re-angles unanswered gaps, depth:'thorough' is paid and chases leads, and the question parameter is explicitly allowed to be broad/multi-part.

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 explicitly distinguishes itself from open-web search and from ask_pipeworx, so an agent can tell exactly what this tool does.

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 gives explicit when-to-use guidance: best for broad/multi-part structured-data questions, use ask_pipeworx for single lookups, and prefer ask_pipeworx for breaking/colloquial current-news topics. It also names the signed-out fallback and depth-tier requirements.

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

All Arcgis Phoenix toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation3/5

Most tools have distinct purposes, but the ask/research family is crowded: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog and require careful description reading to select correctly. Prediction-market tools also overlap in scope, though each has a reasonably distinct angle.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: some are verb_noun (query_layer, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective_noun or brand-prefixed phrases. No consistent verb/noun ordering pattern exists across the set.

Tool Count2/5

34 tools is too many for the apparent focus, especially since the server is named 'Arcgis Phoenix' but only 3 of the tools are actually GIS tools. The bulk is a sprawling Pipeworx data/prediction-market ecosystem plus unrelated utilities, making the set feel over-stuffed and unfocused.

Completeness3/5

The Pipeworx data side is fairly complete for lookups, entity profiles, comparisons, validation, and subscriptions, but the ArcGIS Phoenix portion is only search/schema/query and lacks any analysis, geocoding, or editing capability. The presence of generate_llms_txt and scan_dependency highlights that the overall domain is undefined and therefore hard to call complete.

Resources