packageFetch a single EU open-data dataset record by its portal ID, returning full metadata including title, description, publisher, license, distributions, and tags.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": "eu-trade-data-2023"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Single dataset details from data.europa.eu",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds scope (EU open-data) and return fields but no additional behavioral traits like rate limits or error behavior. This is useful but not rich behavioral context.
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?
Single sentence, front-loaded with the action and resource, then details the return content. No redundant words; every phrase 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 tool is simple with one parameter, comprehensive annotations, and an output schema. The description covers the essential purpose, target, and return fields, making it complete for an agent to select and invoke correctly.
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?
The schema has only 'id' with no description (0% coverage). The description defines it as 'portal ID' and provides the context 'EU open-data dataset record', giving semantic meaning beyond the schema. The schema example further clarifies the expected format, so the parameter is well explained.
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 a specific verb ('Fetch'), a specific resource ('single EU open-data dataset record'), and the identifier ('portal ID'). It also lists the returned metadata fields, making it distinct from sibling tools like 'search' or '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 usage: use when you have a known portal ID and need a single record's full metadata. It doesn't explicitly name alternatives or exclusions, but the 'single' and 'by its portal ID' phrasing contrasts clearly with discovery-oriented sibling tools.
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 tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded are highly similar; deep_research also overlaps with ask_pipeworx. This makes it hard for an agent to distinguish which to use.
Tool names are inconsistent, mixing camelCase (ask_pipeworx, ai_visibility_check) with snake_case (deep_research, compare_entities). Some are verb phrases, others are nouns (groups, tags), with no unified pattern.
With 36 tools covering EU open data, general data retrieval (Pipeworx), and prediction markets (Polymarket), the count is too high for a coherent, focused server. Many tools are redundant or meta-tools.
For a server named 'Data Europa', the EU open-data tools are basic (search, package, groups) lacking update/delete or analysis. The additional Pipeworx/Polymarket tools are extensive but unrelated, making the overall surface incomplete for the implied domain.