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Particle-Academy

fancy-flow-mcp-js

Run Workflow

run_workflow

Execute a workflow graph to test wiring and routing, returning per-node outputs and errors. Validates inputs and rejects malformed graphs.

Instructions

Execute the graph and return per-node outputs plus ok/error. This is a SMOKE TEST of wiring and routing, not a production run: it takes no executors, so nodes fall back to their kind's own behaviour and anything needing a host capability (a terminal session, an LLM client) refuses rather than acting. Refuses outright to run a graph with validation errors, because a malformed graph produces failures that look like engine bugs and are not. Seed entry nodes with initial_inputs, keyed by node id then port.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_idYesThe workflow id.
initial_inputsNoInputs seeded to entry nodes: { "<node_id>": { "<port>": <value> } }.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

There are no annotations, so the description carries the burden of behavioral disclosure. It explains node fallback behavior, refusals for missing host capabilities, and rejection of validation-error graphs. It does not mention persistence, side effects, or mutability, but for a smoke-test runner these omissions are minor.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The definition is compact and front-loaded with the key behavior—outputs and the smoke-test distinction. The subsequent clauses about fallback and refusal are useful clarifications. It is slightly dense, but every clause contributes directly to safe invocation.

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

Completeness4/5

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

For a two-parameter, smoke-test tool with no annotations and no output schema, the description covers purpose, input handling, valid conditions, and the return format of per-node outputs plus status. It offers enough for an agent to call correctly, though an explicit note about side effects/state mutability was absent.

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 coverage is 100% and the schema already describes both workflow_id and initial_inputs, including the nested object shape. The description's 'keyed by node id then port' adds a little emphasis but mostly restates what the schema already documents, so the baseline of 3 is appropriate.

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 starts with an unambiguous verb and object: 'Execute the graph and return per-node outputs plus ok/error.' It then clearly labels itself as 'a SMOKE TEST of wiring and routing, not a production run,' which distinguishes it from lifecycle and validation siblings without needing to open schemas.

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 gives explicit usage context: a smoke test, not a production run, takes no executors, refuses host-dependent capabilities, and rejects invalid graphs. This tells the agent when it is and is not safe to use, though it does not explicitly name an alternative sibling for production-like execution or validation.

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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