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Data Toronto

by io.github.pipeworx-io

Toronto Query

toronto_query
Read-onlyIdempotent

Query any City of Toronto datastore resource (open.toronto.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 toronto_datasets to find a resource id, or toronto_recent for the common ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text search across columns.
sortNoSort clause, e.g. "APPLICATION_DATE desc".
limitNoMax rows (default 100, max 1000).
offsetNoRow offset for paging.
filtersNoExact-match filters as a JSON object, e.g. {"PERMIT_TYPE":"New Building"}.
resource_idYesCKAN datastore resource id (UUID), e.g. "6d0229af-bc54-46de-9c2b-26759b01dd05".

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint=true, destructiveHint=false. The description adds that it queries a datastore and supports various operations, which is consistent and provides additional behavioral context beyond annotations, though it doesn't mention rate limits or other constraints.

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, front-loaded with the main action and supported by clear parameter details. Every sentence is necessary and contributes to understanding.

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?

Given no output schema, the description doesn't explain return values but covers query parameters and pagination. It is complete enough for a query tool with good sibling context. Could mention result format but not required.

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%, but the description adds value by summarizing parameter meanings: 'free-text q', 'exact-match filters', 'sort', limit/offset. The examples in the schema complement this. The description adds meaning beyond the schema's property descriptions.

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 the tool queries a City of Toronto datastore resource by resource ID, with specific parameters like q, filters, sort, limit, offset. It distinguishes from sibling tools by referencing toronto_datasets and toronto_recent for finding resource 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?

The description advises to use toronto_datasets to find a resource ID or toronto_recent for common ones, providing clear context on when to use this tool. However, it does not explicitly state when not to use it or exclude alternatives beyond those mentioned.

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