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Arcgis Tigard

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 5,743 across 1500 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 cover read-only, open-world, idempotent, and non-destructive properties. The description adds substantial behavioral detail: it returns verbatim evidence, emits explicit refusal reasons, and costs one extra LLM call. This goes well beyond what annotations or schema provide.

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?

Dense but efficient: purpose is front-loaded, followed by behavioral guarantees, return shapes, usage guidance, and cost tradeoff. Every sentence contributes actionable information without redundancy.

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 the success and failure return shapes, refusal reason enums, evidence format, and source metadata. Combined with annotations, an agent has everything needed to select and invoke this tool correctly.

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 all six parameters are aliases for the same natural-language question. The description does not add parameter-specific semantics, but the schema already fully documents them, so the baseline 3 is appropriate.

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 precise purpose: a hallucination-resistant answer mode that extracts answers only from tool results, with explicit refusal behavior. It distinguishes itself from the sibling ask_pipeworx by naming it and explaining the grounded-extraction difference.

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 says when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the alternative, ask_pipeworx, and explains the cost tradeoff.

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.

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