column_dataDistinct values in a column.
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes | ||
| resource_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of items returned. | |
| items | Yes | Array of distinct values in the specified column |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "column": "borough",
+ "resource_id": "xj2p-gjci"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Number of items returned.",
+ "type": "integer"
+ },
+ "items": {
+ "description": "Array of distinct values in the specified column",
+ "items": {
+ "description": "Row object containing the column value",
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "items",
+ "count"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds limited behavioral context—it doesn't mention ordering, pagination, or any limitations. With strong annotations, the bar is lower, and the description just barely meets it by clearly stating the core function, but it doesn't go beyond what the annotations convey.
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, grammatically complete sentence of five words. It is perfectly front-loaded and contains zero 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?
The tool is simple with two clearly named parameters and an output schema. Annotations cover safety. The description is adequate for a basic understanding, but it doesn't explicitly state that resource_id identifies the dataset/resource, and it lacks any additional context about sorts, limits, or how distinct values are computed (e.g., nulls excluded). Given the output schema exists, return values are covered, so the description is minimally acceptable but not exemplary.
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 carries the full burden of parameter meaning. The description only references 'column' indirectly ('in a column') and does not explain the resource_id parameter or any formatting/type details. The parameter names are self-explanatory, but the description fails to compensate for the lack of schema descriptions.
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 returns distinct values for a column, and the input schema specifies the required resource_id and column. However, it does not explicitly distinguish from sibling data-access tools like 'query' or 'search_within', though the specific 'distinct values' function is clear.
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 no guidance on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or exclusions. Sibling tools such as 'query' and 'search_within' could serve similar data-retrieval purposes, and the description does not clarify how this tool should be selected over them.
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.
Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim have overlapping functionality in answering factual questions. The detailed descriptions help differentiate them, though some confusion may still arise.
All tool names follow a consistent snake_case convention with a verb_noun pattern (e.g., compare_entities, resolve_entity). No mixing of camelCase or other styles, making the naming predictable and uniform.
34 tools is on the higher side, but many are meta-tools (discover, feedback, subscriptions) and some are redundant (ask_pipeworx vs grounded). While the scope is broad, the count could be trimmed for tighter focus.
The server covers a wide range of domains (SEC, FDA, FRED, prediction markets, etc.) with strong read and analysis capabilities. Missing update/delete operations and direct trading, but comprehensive for data retrieval and analysis.