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Data Montreal

by io.github.pipeworx-io

Montreal Query

montreal_query
Read-onlyIdempotent

Query any City of Montreal datastore resource (donnees.montreal.ca, CKAN) by its resource id (a UUID). Supports a free-text q, exact-match filters (field→value), sort ("field desc"), limit and offset. Use montreal_datasets to find a resource id, or montreal_recent for the common ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text search across columns.
sortNoSort clause, e.g. "DDS_DATE_CREATION desc".
limitNoMax rows (default 100, max 1000).
offsetNoRow offset for paging.
filtersNoExact-match filters as a JSON object, e.g. {"district":"B2"}.
resource_idYesCKAN datastore resource id (UUID), e.g. "2cfa0e06-9be4-49a6-b7f1-ee9f2363a872".

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds that it queries datastore resources and supports various query options. However, it does not disclose return format or pagination details beyond limit/offset, which are already in the schema.

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?

Two well-structured sentences. First sentence captures core purpose and source. Second lists capabilities and links to sibling tools. No wasted words, front-loaded with critical information.

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?

Covers how to use, which parameters, and how to find the required resource ID. Missing return value description, but the tool is straightforward. Given no output schema, a brief note on response format would improve completeness.

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% so baseline is 3. Description summarizes but does not add significant meaning beyond the schema's own parameter descriptions. It reinforces the syntax and purpose but lacks deep additional context.

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?

Clearly states it queries any City of Montreal datastore resource by resource ID. Lists supported features (free-text, filters, sort, limit, offset). Distinguishes from siblings by directing to montreal_datasets and montreal_recent for finding IDs.

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?

Explicitly instructs to use montreal_datasets or montreal_recent to obtain resource IDs, telling the agent when not to use this tool directly. Also explains the parameters briefly, giving clear context for usage.

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

Most tools have distinct purposes with detailed descriptions, but ask_pipeworx_beta is explicitly identical to ask_pipeworx, and the several polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage) have overlapping prediction-market territory. Descriptions help differentiate, but the overlaps could still cause misselection.

Naming Consistency4/5

The majority follow a clear verb_noun snake_case pattern (e.g., compare_entities, resolve_entity, generate_llms_txt). A few tools deviate with noun/adjective prefixes (montreal_datasets, montreal_recent, pipeworx_feedback, recent_alerts) or single verbs (remember, recall, forget), but the overall style is consistent and readable.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold and feels heavy. While each tool has a distinct role, the sheer number—spanning data access, prediction markets, memory, subscriptions, and meta-tools—makes the surface harder for agents to navigate compared to a more focused server.

Completeness4/5

For its broad data-gateway purpose, the server covers a wide range: lookups, research, entity resolution, claim validation, memory, subscriptions, and feedback. The Montreal-specific subset (datasets, query, recent) is adequate for the apparent scope, with only minor gaps like no subscription-update tool.

Resources