Granoflow MCP Server

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by granoflow

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Python

Local

granoflow_task_validate

Validate AI-agent task results to ensure correctness before importing into Granoflow.

Instructions

Validate an AI-agent task result before importing it into Granoflow.

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": {
      "input": "<input>"
    },
    "name": "granoflow_task_validate"
  }
}

Input Schema

Name	Required	Description	Default
`input`	Yes	JSON object sent to the Granoflow Local HTTP API.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 1, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as what validation entails, side effects, required permissions, or error behavior. The short sentence only states the purpose, leaving significant behavioral gaps.

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, efficient sentence that immediately conveys the tool's purpose. No wasted words, and it is front-loaded.

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?

For a validation tool with no output schema and no annotations, the description omits critical context such as input structure requirements, validation criteria, and expected output. This inadequacy risks incorrect invocation by an AI agent.

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

Parameters3/5

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

With 100% schema coverage, the baseline is 3. The tool description adds no additional semantic information beyond the schema's generic 'JSON object sent to the Granoflow Local HTTP API.' No extra context on parameter structure or constraints is provided.

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

Purpose5/5

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

The description clearly states the tool validates an AI-agent task result before importing into Granoflow. It specifies the verb 'validate' and the resource 'task result', effectively distinguishing it from sibling tools like granoflow_task_import and granoflow_task_create.

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 before importing ('before importing it into Granoflow') but provides no explicit guidance on when to use this tool over alternatives, nor any when-not-to-use scenarios. This is minimally adequate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.