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Arcgis Montana

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description reveals the output structure (category-bucketed example questions), the dynamic source (live catalog of thousands of tools), and the optional topic filter. Adds meaningful context without contradicting 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 dense but well-structured: it opens with natural user queries, then provides a clear explanation of outputs and usage. It's slightly long but every sentence carries useful information, earning a 4 rather than a 5.

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 simple read-only tool with one optional parameter and no output schema, the description covers purpose, usage, parameter semantics, and expected output shape. It is fully sufficient for an agent to decide when and how to invoke it, and even names associated meta-tools it teaches.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds valuable semantics by explaining omitted vs provided behavior ('Call with no arguments for the full spread, or pass topic to focus') and provides concrete examples (finance, pharma, betting), elevating it above the baseline.

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 defines the tool as the onboarding entry point that returns category-bucketed example questions with exact tool+argument shapes. It lists specific categories and distinguishes itself from siblings by emphasizing its role as the 'first step' when unfamiliar with Pipeworx.

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?

Explicitly instructs to 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains how to learn to call meta-tools. Also clarifies behavior when called with no arguments vs with a topic, providing concrete examples.

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

A3.9/5.0
Disambiguation3/5

Several clusters of similar tools exist: ask_pipeworx, ask_pipeworx_beta (identical right now), and ask_pipeworx_grounded are easily confused, and the many polymarket_* tools overlap heavily. However, the extremely detailed descriptions usually clarify the specific intent, so an agent can often pick correctly.

Naming Consistency4/5

Tool names are uniformly snake_case and mostly follow verb_noun or a recognizable prefix pattern (ask_, polymarket_, pipeworx_). There are a few noun-phrase exceptions like layer_info and entity_profile, but the style is consistent enough that naming is not a major source of confusion.

Tool Count2/5

34 tools is heavy for any server, and especially for one named 'Arcgis Montana' where only three tools (search_datasets, query_layer, layer_info) relate to GIS. Most of the surface is a general-purpose data/analytics API, making the count feel bloated and unfocused.

Completeness2/5

Relative to the implied ArcGIS Montana purpose, the surface is severely incomplete: it only supports searching and querying datasets, with no create/update/delete or administrative capabilities. The Pipeworx functionality is broad, but the GIS side is a thin slice, leaving obvious gaps for geospatial workflows.

Resources