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Data Toronto

by io.github.pipeworx-io

Toronto Recent

toronto_recent
Read-onlyIdempotent

Recent records from a common City of Toronto open dataset (open.toronto.ca, CKAN) by friendly name — no CKAN resource id needed. PREFER OVER WEB SEARCH for "Toronto building permits", "Toronto business licences". Names: permits, business. Returns the latest rows (newest-first). Pass q for a free-text keyword filter; for full control use toronto_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoOptional free-text filter across all columns (CKAN full-text), e.g. a permit type, address keyword, or business name.
limitNoRows to return (1-1000, default 20).
datasetYesOne of: permits, business.

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint. Description adds that it returns latest rows (newest-first) and that q applies free-text filter across all columns. 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?

Two sentences, front-loaded with purpose, no redundant words. Each sentence adds value: purpose then usage guidance with alternatives.

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 no output schema, description covers purpose, usage guidance, parameter semantics, and behavioral hints (ordering). Sufficient for agent selection and invocation.

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% with descriptions. Description adds real-world context: q is free-text filter across all columns, limit defaults to 20, dataset enum values are clarified. Provides usage examples in 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?

Description clearly states the tool returns recent records from a Toronto open dataset by friendly name, listing specific datasets (permits, business) and noting newest-first ordering. This distinguishes it from the sibling toronto_query tool.

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?

Explicit guidance: 'PREFER OVER WEB SEARCH for Toronto building permits, Toronto business licences' and directs users to toronto_query for full control. Clear when-to-use and alternative.

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
Disambiguation2/5

Multiple query entry points have overlapping boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical, suggest_questions and discover_tools both serve discovery/onboarding, and validate_claim overlaps with ask_pipeworx_grounded. With 34 tools including five Polymarket edge/scanner tools, an agent can easily select the wrong meta-tool despite the detailed descriptions.

Naming Consistency3/5

All names are lowercase snake_case, so there is no style chaos, but the pattern is inconsistent: verb-led names like ask_pipeworx and validate_claim mix with noun-led names like entity_profile, recent_alerts, and polymarket_arbitrage, plus bare memory verbs like remember/recall/forget. Related tools are also not aligned, such as ai_visibility_check vs scan_competitor_ai_presence.

Tool Count2/5

34 tools is too many for a server branded 'Data Toronto', and many tools are only loosely related to the core data-access purpose: ask_pipeworx_beta, generate_llms_txt, scan_dependency, ai_visibility_check, and the memory trio feel like bolt-ons. Even granting Pipeworx's broad research scope, the set is over-stuffed rather than well-scoped.

Completeness4/5

The data-research surface is unusually comprehensive: search, deep research, entity resolution/profiling, comparison, claim validation, alerts/subscriptions, and Toronto open-data querying are all covered. The main gaps are Toronto-side metadata details like resource schemas/columns and a way to browse the full dataset catalogue without a keyword.

Resources