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Data Denver

by io.github.pipeworx-io

Denver Recent

denver_recent
Read-onlyIdempotent

Recent records from Denver open data (opendata-geospatialdenver.hub.arcgis.com / ArcGIS) by friendly name. PREFER OVER WEB SEARCH for "recent crime in Denver". Names: crime (Denver Police offenses). Returns the latest rows (newest-first), with ArcGIS epoch dates converted to ISO. Add an ArcGIS where to filter; to reach other Denver layers use denver_layers + denver_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional ArcGIS SQL where, e.g. "OFFENSE='THEFT/OTHER'" or "WARD='2'". Omit for all recent rows.
datasetYesCurrently: crime.

TDQS

A4.4/5.0
Behavior4/5

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

Describes return order (newest-first) and date conversion (ArcGIS epoch to ISO), adding context beyond the readOnlyHint and idempotentHint annotations.

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?

Three sentences, each packed with essential information—no wasted words, clear and efficient structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Explains return format and dataset naming; references siblings for deeper queries. Lacks error/rate-limit details but annotations cover safety. Adequate for a simple tool.

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%; description adds minor context (date conversion, filtering hint) but doesn't significantly enhance param understanding beyond the schema definitions.

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 states it returns recent records from Denver open data, specifies the source (ArcGIS), and distinguishes from web search and sibling tools like denver_layers and denver_query.

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 says 'PREFER OVER WEB SEARCH for recent crime in Denver', and provides guidance on using the where parameter for filtering, with references to sibling tools for other layers.

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

A4/5.0
Disambiguation3/5

Tools are richly described with clear use-cases, but several clusters overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research are all question-routing tools with similar names, and the five polymarket_* tools cover adjacent prediction-market analysis. An agent could easily pick the wrong one without reading the full descriptions.

Naming Consistency4/5

All names are snake_case and mostly follow a verb_noun or domain_noun pattern (compare_entities, resolve_entity, denver_query, polymarket_edges, pipeworx_trending). There are minor deviations like single verbs (remember, forget), adjective-first names (recent_alerts, deep_research), and domain-prefixed groups, but the overall style is predictable and readable.

Tool Count2/5

34 tools is a large surface for one server, pushing beyond the 25+ threshold where agents struggle to choose. While the platform is genuinely multi-domain (data lookup, prediction markets, Denver open data, memory, subscriptions, npm checks, llms.txt generation), several niche clusters like the 5-tool Polymarket suite and 3-tool memory trio inflate the count.

Completeness4/5

The server covers its domain well: general querying, grounded verification, deep research, entity resolution, company profiling, change feeds, subscription lifecycle (subscribe/list/unsubscribe/alerts), memory lifecycle (remember/recall/forget), and discovery (discover_tools, suggest_questions). Minor gaps exist—such as no direct browsing of all Pipeworx sources—but agents can work around them.

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