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Arcgis Phoenix

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,735 across 1498 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this as read-only, idempotent, open-world, and non-destructive, but the description adds valuable behavioral detail: it extracts only from tool results, returns verbatim evidence and confidence, and provides explicit structured refusals with specific reason codes. This far exceeds the bare annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: it opens with the core purpose, then details behavior, return shape, refusal semantics, and usage guidance. Every sentence carries essential information for correct invocation and expectation-setting, with no fluff.

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 having no output schema, the description fully specifies both success and refusal return shapes, explains the routing mechanism, and covers when to use this tool versus the alternative. An agent has everything needed to invoke it correctly and interpret results.

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 coverage is 100% with all six parameters documented as aliases for the same natural-language question. The description adds no parameter-level meaning beyond the schema, which is exactly the baseline case where the schema does the heavy lifting.

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 clearly identifies this as a hallucination-resistant answer mode for high-stakes reads, explicitly distinguishing it from ask_pipeworx via grounded extraction and explicit refusals. The verb and resource are specific, and the differentiation from siblings is unambiguous.

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?

It explicitly instructs agents to use this tool when answers will be quoted, cited, or acted on, and to prefer ask_pipeworx for casual lookups. It also discloses the extra LLM call cost, giving a concrete trade-off for tool selection.

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

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