instance_infoFetch catalog-level metadata for an Opendatasoft portal instance (total dataset count, themes, languages); defaults to public.opendatasoft.com.
| Name | Required | Description | Default |
|---|---|---|---|
| instance | No |
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Instance name | |
| records_count | No | Total number of records across all datasets | |
| datasets_count | No | Total number of datasets |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "instance": "public.opendatasoft.com"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Instance metadata and catalog information",
+ "properties": {
+ "datasets_count": {
+ "description": "Total number of datasets",
+ "type": "number"
+ },
+ "name": {
+ "description": "Instance name",
+ "type": "string"
+ },
+ "records_count": {
+ "description": "Total number of records across all datasets",
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds the default instance behavior and specifies the kind of metadata returned. This goes beyond annotations by clarifying scope and default, though it omits details like error handling or auth requirements.
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 one sentence, front-loaded with the verb and resource, and includes a default. Every word earns its place with no fluff or repetition of schema or annotation details.
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 (one optional param, no nested objects, output schema present). The description covers the main inputs, default behavior, and key output elements. It lacks mention of error cases or potential limitations, but given the low complexity and annotations, this is nearly complete.
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?
Schema description coverage is 0%, so the description must compensate. It clarifies that the 'instance' parameter identifies a portal and mentions the default, but does not specify accepted formats or value constraints beyond the schema's example. This is minimal but useful compensation for a single optional parameter.
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 uses a specific verb ('Fetch') and resource ('catalog-level metadata for an Opendatasoft portal instance') with concrete details (total dataset count, themes, languages). It clearly distinguishes itself from sibling tools like 'datasets' and 'records' which handle data-level queries.
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 context is clear: this is for portal-level metadata, not dataset-level details. The default to public.opendatasoft.com provides practical guidance. However, it does not explicitly name alternative tools or state when not to use it, though the sibling list offers obvious contrasts.
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.
The tool set is a kitchen sink of unrelated utilities (Opendatasoft catalog, Pipeworx data search, prediction markets, npm scanning, memory, etc.). The 'ask_pipeworx' family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar and could easily be confused. The wide variety of purposes with overlapping names makes it hard for an agent to disambiguate.
Naming is wildly inconsistent: snake_case (ai_visibility_check, ask_pipeworx), concatenated (pipeworx_trending, polymarket_arbitrage), verb phrases (compare_entities, suggest_questions), and simple nouns (dataset, records). No consistent pattern exists, making it hard to predict tool names.
At 36 tools, the server is overloaded with a scattershot collection of capabilities unrelated to its name (Opendatasoft). Only 5 tools directly relate to Opendatasoft, while the rest cover diverse third-party services. This indicates poor scope focus.
The server lacks completeness for any single purpose. For Opendatasoft, it has only read-oriented tools with no create/update/delete. For Pipeworx, many query tools exist but no data ingestion. Prediction market tools are extensive but not part of the core mission. Overall, the surface has significant gaps.