toronto_datasetsSearch the City of Toronto open-data catalogue (open.toronto.ca, CKAN) by keyword. Returns each matching dataset's title and its queryable datastore resource ids (use with toronto_query).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-50, default 15). | |
| query | No | Keyword(s), e.g. "parking", "trees", "budget". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description does not need to repeat those. The description adds value by specifying the source (open.toronto.ca via CKAN) and the return format (titles and resource IDs), which is behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words. The first sentence describes the core action and source, and the second explains the output and provides a usage hint. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's role (search by keyword), output (titles and resource IDs), and linkage to 'toronto_query'. It is sufficient for a low-complexity tool with full schema coverage and strong annotations. Minor omission: no mention of result ordering or pagination, but the limit parameter handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100%, with clear descriptions for 'query' and 'limit'. The description adds minimal semantic value beyond the schema, only mentioning 'keyword' which maps to 'query'. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search' and the specific resource 'City of Toronto open-data catalogue' by keyword. It also distinguishes itself from sibling tools by explicitly linking to 'toronto_query' and implying its role in the pipeline of finding then querying datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance by saying 'use with toronto_query', which tells the agent exactly when to invoke this tool and what to do with the results. It also implicitly defines when not to use it (i.e., when you already have dataset IDs) and distinguishes it from 'toronto_recent'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.