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SoloFlow

SoloFlow

by halexzd686-cloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Validate Flow

soloflow_validate_flow

Validate a Flow file by checking DAG structure, detecting cycles, and verifying step references to ensure a correct workflow topology.

Instructions

校验一个 Flow 文件——检查 DAG 结构、循环依赖、步骤引用。返回校验结果和拓扑层级。

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": {
      "name": "<name>"
    },
    "name": "soloflow_validate_flow"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesFlow 名称

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It discloses the core behaviors: checking DAG structure, circular dependencies, step references, and returning validation results plus topological hierarchy. It implies a non-destructive validation operation, though it could be more explicit about being read-only.

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 two short sentences, front-loading the purpose and stating the return value. There is no redundant information, making it highly efficient.

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

Completeness5/5

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers all necessary context: what it validates, the specific checks, and what it returns. It is fully sufficient for an agent to understand and invoke the tool.

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?

The only parameter 'name' is fully described in the schema (Flow 名称), so schema coverage is 100%. The description does not add any additional semantic meaning beyond what the schema already provides, so the baseline of 3 applies.

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 a Flow file, specifying the exact checks (DAG structure, circular dependencies, step references) and the return value (validation result and topological hierarchy). It explicitly distinguishes this from the sibling tool soloflow_validate_skill.

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

Usage Guidelines4/5

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

The description clearly implies when to use it (when you need to validate a Flow file) but does not explicitly contrast it with alternatives like soloflow_validate_skill or state when not to use it. Still, the context is clear and not misleading.

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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