query_tablePull data from a table. body is a PxWeb query object.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | {query: [{code, selection: {filter, values}}], response: {format: "json-stat2"}} | |
| path | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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": [
+ "00"
+ ]
+ }
+ },
+ {
+ "code": "År",
+ "selection": {
+ "filter": "top",
+ "values": [
+ "1"
+ ]
+ }
+ }
+ ],
+ "response": {
+ "format": "json-stat2"
+ }
+ },
+ "path": "BE/BE0101/BE0101A/BefolkningNy"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Query result in JSON-Stat 2 format",
+ "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. The description adds minimal behavioral context (just 'pull data' and the query object format). It does not explain output format, error handling, or any side effects beyond what annotations imply.
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 concise at two sentences with no redundant words. It efficiently communicates the core action and the nature of the body parameter.
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?
Despite having an output schema, the description does not mention the return format or provide enough context for a domain-specific tool (PxWeb). The minimal description leaves the agent with insufficient information to form a complete mental model.
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 description adds meaning by stating the body is a 'PxWeb query object', which contextualizes the nested schema. However, with schema coverage at 50%, it does not describe the 'path' parameter, leaving some ambiguity.
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 pulls data from a table and mentions the body is a PxWeb query object, providing a specific verb and resource. However, it does not differentiate from the sibling tool 'table_meta', which likely retrieves metadata instead of data.
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?
No guidance is provided on when to use this tool versus alternatives like 'table_meta' or other search tools. The description lacks context on appropriate use cases or when not to use it.
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 clearly distinct purposes, but several overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (only differing in verification/depth), and polymarket_edges / polymarket_arbitrage / polymarket_edge_tracker / polymarket_fill_risk / polymarket_kalshi_spread all target similar prediction-market signals, which could cause mis-selection without careful reading.
Most names follow a clear verb_noun pattern (resolve_entity, query_table, remember, recall, forget, subscribe, unsubscribe, validate_claim, compare_entities, search_within), but there are exceptions like ai_visibility_check (adjective_noun), generate_llms_txt (verb_noun with dot), and several polymarket_* names that are fine but inconsistent with the snake_case verb-first convention.
35 tools is a large but reasonable surface for a broad data/serach platform covering company financials, economics, prediction markets, memory, subscriptions, and discovery. The count is justified by the wide domain, and the set is not bloated with trivial duplicates.
The surface covers core CRUD for entities (resolve, profile, compare, search, query) and memory (remember/recall/forget), plus subscriptions and meta-tools. Minor gaps: no explicit tool for updating/creating entities (understandable for a read-only data service), and no tool for listing all available table schemas beyond discovery (subjects covers this). Overall strong coverage.