list_groupsList thematic groups/categories (Italian themes like Agricoltura, Salute, Trasporti) on dati.gov.it (CKAN group_list, with full fields).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint true, so the tool is safe. The description adds that it uses CKAN group_list with full fields, providing technical detail and return shape beyond 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?
The description is a single sentence that efficiently conveys purpose, scope, examples, and technical source without any wasted words.
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?
Given the tool has no parameters and no output schema, the description is complete: it explains the source, the nature of the data, and that full fields are returned. No gaps exist for its simplicity.
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?
With 0 parameters, the input schema is vacuous. The description correctly adds no additional parameter info, meeting the baseline of 4 for zero-parameter tools.
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 tool lists thematic groups/categories on dati.gov.it, with specific examples like Agricoltura, Salute, Trasporti. It uses a specific verb-resource combination and differentiates from sibling tools like list_organizations by focusing on groups.
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 implies when to use (for listing thematic groups) but does not explicitly state alternatives or exclusions. The contrast with sibling list_organizations is inferable but not explicit.
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.
Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same 5,529 tools; polymarket_arbitrage and polymarket_edges both find tradeable opportunities; discover_tools and suggest_questions both serve discovery. The beta tool being an exact duplicate makes misselection highly likely.
Most action tools follow verb_noun (ask_pipeworx, compare_entities, discover_tools, list_groups, resolve_entity, search_datasets, suggest_questions, validate_claim), but there is significant mixing with noun_noun (dataset_details, entity_profile, organization_details, pipeworx_feedback, polymarket_arbitrage) and adjective_noun (deep_research, recent_alerts). The Polymarket family is consistently prefixed, but overall the server mixes several conventions.
36 tools far exceeds the typically well-scoped range, and the server bundles what appear to be five separate concerns: Italian open data, Pipeworx universal query, entity/report utilities, prediction-market analytics, and meta/memory/subscription features. Many tools could be consolidated (e.g., ai_visibility_check and scan_competitor_ai_presence; discover_tools and suggest_questions), making the set feel bloated.
The broad domain of structured data research and prediction-market edge is largely covered: universal routing, grounded answers, deep research, entity resolution, profiles, comparisons, change feeds, claim verification, arbitrage scans, fill-risk, subscriptions, memory, and feedback. Minor gaps exist—no direct tool to fetch raw CKAN resource URLs, no exhaustive list of all 5,529 tools, and no actual order execution on prediction markets—but these are workable around.