organizationsList EU open-data portal publishing organizations (EU institutions, member-state agencies, etc.) with their names and dataset counts; limit controls page size (default 100, max 1000).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Array of organizations |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "limit": 10
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "List of publishing organizations",
+ "properties": {
+ "data": {
+ "description": "Array of organizations",
+ "items": {
+ "description": "Organization metadata",
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context about pagination (default 100, max 1000) and the return fields (names, dataset counts), which goes 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?
The description is a single sentence that front-loads the main purpose and follows with parameter semantics. No wasted words, every part 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?
Given the tool's simplicity (one optional parameter), the rich annotations, and the presence of an output schema, the description provides all necessary context for an agent to select and invoke the tool correctly. It covers purpose, parameter behavior, and expected output content.
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 provides only the type 'number' for 'limit', so schema coverage is 0%. The description fully compensates by explaining that limit controls page size with default 100 and max 1000, adding critical meaning beyond the raw schema.
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 EU open-data portal publishing organizations with names and dataset counts, which is specific and distinguishes from sibling tools like 'groups' or 'search'. The verb 'List' plus resource scope makes the purpose unambiguous.
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 gives clear context for when to use (listing EU organizations) and details on the limit parameter, but does not explicitly mention alternatives or exclusions. The usage is implied but not fully contrasted with similar 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.