query_tablePull data from a table (max 50,000,000 cells). path is the .px table id; body is a PxWeb query object.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | {query: [{code, selection: {filter, values}}], response: {format: "json-stat2"}} | |
| path | Yes | e.g. "0156101S.px" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe read-only behavior. The description adds the concrete maximum cell limit, which is valuable operational detail beyond annotations. No contradictions detected.
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?
Two concise sentences, front-loaded with action and limit. Every word serves a purpose: verb, resource, constraint, parameter definitions.
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?
Adequate but missing details: no mention of output format (though example in schema shows json-stat2), no guidance on handling large results or errors, and no description of the query object structure beyond schema.
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 coverage is 100%, so baseline is 3. The description adds meaning by explaining path as '.px table id' and body as 'PxWeb query object', providing domain context not in schema.
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 ('Pull data') and the resource ('from a table'), with a specific constraint (max 50,000,000 cells). It distinguishes from sibling table_meta by focusing on data retrieval vs metadata, though this is implicit.
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 explicit guidance on when to use this tool versus alternatives like table_meta or search_within. The description implies usage for data pulling but provides no context for 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.
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Most tools have clearly distinct purposes, but there is some overlap among ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as between discover_tools and suggest_questions. However, detailed descriptions help differentiate them.
Naming conventions are highly inconsistent, mixing snake_case (ask_pipeworx, forget), camelCase (discoverTools, suggestQuestions), and underscores (ai_visibility_check, compare_entities). No predictable pattern.
33 tools is on the high side, but the broad domain (finance, pharma, prediction markets, etc.) partly justifies it. However, some tools like forget, remember, recall seem generic and could be separated.
The tool surface covers a wide range of functionalities: visibility checks, pipeworx queries, entity profiles, comparisons, subscriptions, memory, and more. Minor gaps may exist in real-time data or specific niche sources.