facetsRetrieve distinct values and counts for a named facet field within an Opendatasoft dataset; useful for enumerating categories or filtering options before querying records.
| Name | Required | Description | Default |
|---|---|---|---|
| facet | Yes | ||
| instance | No | ||
| dataset_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| facets | No | Array of facet items |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "dataset_id": "world-cities",
+ "facet": "country"
+ },
+ {
+ "dataset_id": "us-census-data",
+ "facet": "state",
+ "instance": "public.opendatasoft.com"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Facet distinct values and counts",
+ "properties": {
+ "facets": {
+ "description": "Array of facet items",
+ "items": {
+ "properties": {
+ "count": {
+ "description": "Number of records with this value",
+ "type": "number"
+ },
+ "name": {
+ "description": "Facet value",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {
- "dataset_id": "world-cities",
- "facet": "country"
- },
- {
- "dataset_id": "us-census-data",
- "facet": "state",
- "instance": "public.opendatasoft.com"
- }
-]Output schema / (root)Previous value: -{
- "description": "Facet distinct values and counts",
- "properties": {
- "facets": {
- "description": "Array of facet items",
- "items": {
- "properties": {
- "count": {
- "description": "Number of records with this value",
- "type": "number"
- },
- "name": {
- "description": "Facet value",
- "type": "string"
- }
- },
- "type": "object"
- },
- "type": "array"
- }
- },
- "type": "object"
-}New value: +nullInput schema / examplesAdded value: +[
+ {
+ "dataset_id": "world-cities",
+ "facet": "country"
+ },
+ {
+ "dataset_id": "us-census-data",
+ "facet": "state",
+ "instance": "public.opendatasoft.com"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Facet distinct values and counts",
+ "properties": {
+ "facets": {
+ "description": "Array of facet items",
+ "items": {
+ "properties": {
+ "count": {
+ "description": "Number of records with this value",
+ "type": "number"
+ },
+ "name": {
+ "description": "Facet value",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive, covering safety. The description adds the core output behavior (distinct values and counts) but does not disclose potential limitations like pagination, ordering, or count accuracy.
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 two sentences, front-loaded with the main verb and resource, and contains no wasted words. It is concise and well-structured.
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 is adequate for a read-only facets tool but has significant gaps in parameter semantics, especially for 'instance'. Given no schema descriptions, the tool is not fully self-contained for autonomous use.
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 zero property descriptions, so the description must compensate. It implicitly covers 'facet' and 'dataset_id' but leaves 'instance' unexplained, which is ambiguous and critical for correct invocation.
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 action (retrieve), resource (distinct values and counts for a facet field within an Opendatasoft dataset), and purpose (enumerating categories or filtering options). This distinguishes it from sibling tools like 'records' that query actual records.
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 clear context on when to use this tool ('before querying records'), but does not explicitly name alternative tools or state when not to use it. This gives sufficient guidance without exclusions.
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.