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Arcgis Tigard

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 account/paid-tier requirement, latency range, citation conditions (citation_uri present only when resolvable), gaps[] never fabricated, contradictions[] only for standard/thorough, and semantic excerpting. ReadOnly/idempotent annotations are not contradicted and the description adds much context beyond 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?

Information-dense and front-loaded with the account requirement and alternative tool, but the block is long and repeats ask_pipeworx/gaps[] several times. Slightly verbose, though every clause adds operational value.

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?

Despite no output schema, the description specifies the findings packet fields (verbatim evidence, confidence, source, fetched_at, citation, hop), gaps[] semantics, contradictions[], and performance expectations. For a complex tool with no output schema, nothing essential for correct invocation 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?

The schema already fully documents question and depth, but the description enriches depth with behavior ('standard re-angles unanswered gaps', 'thorough additionally chases leads') and latency, and clarifies that broad questions are accepted. This is above the baseline of 3 for 100% coverage.

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 1497 STRUCTURED data sources') and explicitly contrasts with open-web search. Names sibling ask_pipeworx as the single-lookup alternative, so it is clearly distinguishable.

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?

Explicit when-to-use ('Best for broad/multi-part questions over structured data'), when-not-to-use ('For a single lookup use ask_pipeworx', 'For BREAKING... topics, prefer ask_pipeworx'), and account prerequisite ('If you are not signed in, use ask_pipeworx instead').

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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the various ask_pipeworx variants and entity research tools (entity_profile, compare_entities, recent_changes). The subtle differences between ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are likely to cause agent misselection.

Naming Consistency2/5

Tool names use inconsistent patterns: snake_case (ask_pipeworx, query_layer) mixed with descriptive phrases (ai_visibility_check, generate_llms_txt) and no clear verb_noun structure. Some names are vague (process, run) though those are absent here; overall naming is arbitrary.

Tool Count2/5

34 tools is too many for an Arcgis Tigard server. The majority are generic Pipeworx data query tools (27+ tools) that have little to do with ArcGIS, making the tool count feel bloated and unfocused for the server's stated purpose.

Completeness1/5

The server severely lacks ArcGIS-specific functionality. Only three tools (search_datasets, query_layer, layer_info) are relevant to ArcGIS; the rest are unrelated Pipeworx tools. Essential ArcGIS operations like editing, analysis, or visualization are missing, making the surface incomplete.

Resources