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SoloFlow

SoloFlow

by halexzd686-cloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Run Flow

soloflow_run_flow

Execute a Flow workflow with DAG orchestration, running parallel and sequential steps automatically. Use dry_run to preview the execution plan before actual LLM calls.

Instructions

执行一个 Flow 工作流。Flow 会按照 DAG 编排自动执行多个步骤——并行步骤同时运行，串行步骤按序运行。支持 dry_run 预览。

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_run_flow"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesFlow 名称，如 blog-pipeline、code-review
inputsNo输入参数，key-value 形式，如 {"topic": "AI Agent"}
dry_runNo仅预览执行计划，不实际调用 LLM

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that running a Flow involves DAG orchestration and supports dry_run (which implies it may otherwise call LLMs). However, it doesn't mention side effects like actual execution results, potential costs, or return value behavior, leaving some ambiguity for an agent.

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 concise sentences, front-loaded with the primary purpose. It efficiently conveys the essential behavioral model (DAG, parallel/serial) and dry_run support with no wasted words.

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?

Given there is no output schema, the description should explain what the tool returns, but it does not. It also omits prerequisites (e.g., the flow should already exist or be validated) and whether execution is synchronous or long-running. This is a significant gap for a run tool with no annotations.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds no significant parameter semantics beyond the schema; it only reiterates the dry_run preview concept already documented. The schema itself provides adequate descriptions for name, inputs, and dry_run.

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 executes a Flow workflow (执行一个 Flow 工作流), using a specific verb and resource. It distinguishes from sibling tools like list_flows and validate_flow by focusing on running, and adds detail about DAG orchestration and parallel/serial execution.

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 provides clear context for when to use this tool: to execute a Flow, with details about how it handles parallel and serial steps. It doesn't explicitly mention alternatives or exclusions, but the context is sufficient for an agent to infer the appropriate use case.

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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