query_tablePull data from a table. id is the numeric SSB table id; body is a PxWeb query object. Omitted dimensions with elimination collapse; otherwise select values per dimension.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Numeric table id, e.g. "07459". | |
| body | Yes | {query: [{code, selection: {filter: "item", values: [...]}}], response: {format: "json-stat2"}} |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "body": {
+ "query": [
+ {
+ "code": "Region",
+ "selection": {
+ "filter": "item",
+ "values": [
+ "0301"
+ ]
+ }
+ }
+ ],
+ "response": {
+ "format": "json-stat2"
+ }
+ },
+ "id": "07459"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond those annotations by explaining that omitted dimensions 'collapse' in the result. This is valuable behavioral information not present in the schema or 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 three short sentences, front-loaded with the core purpose. Each sentence provides distinct, necessary information: what it does, how parameters map, and a key behavioral rule. There is no filler or redundancy.
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?
With strong annotations, a complete schema example, and a nested body structure, the description is largely adequate. The omission semantics are explained, but the output shape is not explicitly described and the relationship to table_meta is absent, leaving minor gaps.
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 100%, giving a baseline of 3. The description enriches both parameters by identifying 'id' as the SSB table id and 'body' as a PxWeb query object, plus explaining the behavior of omitted dimensions. This adds clarity beyond the schema's structural definitions.
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's purpose with a specific verb and resource: 'Pull data from a table.' It also identifies the SSB table id and PxWeb query object, which distinguishes it from generic data tools. However, it does not explicitly contrast with sibling tools like table_meta, so it misses the full differentiation.
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 such as table_meta or search_within. The only usage-related note is about omitted dimensions collapsing, which is more about parameter construction than choosing between 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.
Each tool has a clearly distinct purpose with detailed descriptions that explain when to use which. Overlapping tools like ask_pipeworx vs ask_pipeworx_grounded are explicitly differentiated by use case (casual vs high-stakes). The Polymarket tools are highly specialized and non-overlapping.
All tool names follow a consistent snake_case pattern with descriptive terms (e.g., ask_pipeworx, resolve_entity, validate_claim). There is no mixing of camelCase or other conventions, making the names predictable and easy to parse.
With 33 tools, the count is above the typical 3-15 range, but it is justified by the server's broad scope covering multiple domains (SEC, FDA, FRED, prediction markets, etc.) and includes meta-tools for discovery and monitoring. Each tool seems necessary for the overall functionality.
The tool surface covers a comprehensive range of operations: data querying, entity profiles, comparisons, monitoring, memory, search, and even feedback. It includes both general-purpose and specialized tools, leaving no obvious gaps for the stated purpose of authoritative data retrieval and analysis.