Qingflow MCP (CRUD)

Qingflow Canonical Query Aggregate

qf.query.aggregate

Read-onlyIdempotent

Aggregate records by grouping fields and computing metrics such as count, sum, avg, min, max. Returns metrics per column and a query handle for further processing.

Instructions

Aggregate records through the canonical DSL and return metrics_by_column plus query handle resources.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "plan_id": "<plan_id>"
    },
    "name": "qf.query.aggregate"
  }
}

Input Schema

Name	Required	Description	Default
`plan_id`	Yes
`query`	No

Output Schema

Name	Required	Description	Default
`ok`	Yes
`data`	Yes
`meta`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.0May 28, 2026

TDQS

B3.1/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety. The description adds value by stating the return type (metrics_by_column and query handle resources), but it does not explain pagination, error behavior, or other traits 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, self-contained sentence. It is concise and front-loaded, containing no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema (not shown), the tool has a complex nested input schema and no parameter descriptions. The description lacks essential context about the DSL structure, parameter constraints, and usage scenarios, making it incomplete for an agent to reliably construct a valid query.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate, but it provides no parameter details at all. It mentions 'canonical DSL' but does not explain the required fields (e.g., group_by, metrics) or optional parameters, leaving the agent with no semantic guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it aggregates records using the 'canonical DSL' and specifies the return type ('metrics_by_column plus query handle resources'). This distinguishes it from sibling tools like qf.query.rows (for rows) and qf.query.record (for single records), though it does not explicitly contrast them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for aggregation tasks, but it provides no explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. The context from sibling names offers some implicit differentiation.

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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