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Data Denver

by io.github.pipeworx-io

Denver Query

denver_query
Read-onlyIdempotent

Query any Denver ArcGIS layer by service path + layer id. Full ArcGIS query: where, out_fields, order_by, limit. Use denver_layers to find a service/layer, or denver_recent for the common ones. Epoch dates are converted to ISO.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
layerYesLayer id within the service (from denver_layers), e.g. 324.
limitNoMax rows (default 100, max 2000).
whereNoArcGIS SQL where (default "1=1").
serviceYesArcGIS service path, e.g. "ODC_CRIME_OFFENSES_P/FeatureServer" (or a short name: crime).
order_byNoSort clause, e.g. "FIRST_OCCURRENCE_DATE DESC".
out_fieldsNoComma-separated fields, or "*" (default).

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds: 'Epoch dates are converted to ISO.' This is a behavioral trait beyond annotations. No contradictions.

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 two sentences: the first defines core purpose, the second adds usage guidance and a key detail. Every sentence is essential and front-loaded. No redundant words.

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?

The tool has 6 parameters (2 required), no output schema. The description explains the query capability, links to related tools, and mentions date conversion. It covers the main use case. Minor gaps: no mention of error handling or response structure, but these are less critical for a query tool with clear parameter semantics.

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 coverage is 100%, so the schema documents all parameters. The description adds value by providing examples (e.g., 'service: crime' as a short name), defaults (where: '1=1', limit: 100, max: 2000), and format guidance for order_by. This enriches understanding beyond the raw schema.

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: 'Query any Denver ArcGIS layer by service path + layer id.' It specifies the verb (query), resource (Denver ArcGIS layer), and scope (by service path and layer id). It distinguishes itself from sibling tools like denver_layers and denver_recent by focusing on query execution.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises: 'Use denver_layers to find a service/layer, or denver_recent for the common ones.' This provides clear context and alternatives. It does not explicitly state when not to use the tool, but the guidance is strong.

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.

Resources