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Data Montreal

by io.github.pipeworx-io

Montreal Recent

montreal_recent
Read-onlyIdempotent

Recent records from a common City of Montreal open dataset (donnees.montreal.ca, CKAN) by friendly name. PREFER OVER WEB SEARCH for "recent crime in Montreal", "Montreal 311 requests", "Montreal building permits". Names: 311, crime, permits. Returns the latest rows (newest-first). Pass q for a free-text filter; for full control use montreal_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoOptional free-text filter across all columns (CKAN full-text), e.g. "larceny" or a neighborhood.
limitNoRows to return (1-1000, default 20).
datasetYesOne of: 311, crime, permits.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already provide safety info (readOnlyHint, idempotentHint, etc.). Description adds useful context like 'Returns the latest rows (newest-first)' and explains the free-text filter via `q`. No contradictions. However, it could mention pagination or error handling but overall sufficient.

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 earning its place: purpose, usage guidance, and parameter info. No extraneous text. Front-loaded with key information.

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

Completeness3/5

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

Given no output schema, the description only vaguely states 'returns the latest rows'. It doesn't specify return format, error handling, or rate limits. However, the tool is simple and annotations cover safety. For completeness, mention of output structure would be helpful, so a 3 is appropriate.

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 baseline is 3. Description adds value by explaining that `q` is a free-text filter across all columns and lists dataset enum values. Examples further clarify usage. This goes 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 clearly states it retrieves 'recent records' from a specific source (Montreal open dataset) and lists exact datasets (311, crime, permits). It distinguishes itself from the sibling tool 'montreal_query' by stating 'for full control use montreal_query', so the agent knows when to use which.

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 tells the agent to 'PREFER OVER WEB SEARCH' for certain queries, and contrasts with 'montreal_query' for full control. This provides clear guidance on when to use this tool versus alternatives.

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