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Particle-Academy

fancy-flow-mcp-js

Validate Workflow

validate_workflow

Check a workflow and get issues tagged by source: schema issues require editing the graph; host issues require changing capability or workflow kind.

Instructions

Check a workflow and return every issue TAGGED by who refused it: source "schema" means the graph is malformed and editing it is the fix; source "host" means this host will not run that kind and the fix is a capability or a different kind. Collapsing the two costs you the wrong fix.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_idYesThe workflow id.

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description must bear the behavioral disclosure burden. It does: it clarifies that each issue is tagged with its refusing source, that 'schema' means malformed graph, and that 'host' means a capability mismatch—distinguishing the two is critical to choosing the right fix. It does not explicitly mention read-only status, but 'check' and 'return' make mutation unlikely, and the richer context about issue sources is extremely valuable beyond any structured metadata.

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?

Three sentences, each earning its place: the first states the action, the second states the two issue sources and their fixes, and the third warns against collapsing them. There is no filler, and the most important statement—the cheaper message—is in the final strong sentence. This is concise without being under-specified.

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 single-parameter tool with no output schema and no annotations, this description is largely complete: it explains what the tool returns, the meaning of the return values in the two categories, and the practical consequence of using that output. Minor gaps are the lack of an explicit read-only declaration and no guidance about whether the graph's state is checked asynchronously or instantly with the result.

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?

There is only one parameter ('workflow_id') and schema_description_coverage is 100%, so the schema fully documents it. The description adds no parameter-specific detail beyond referring to the workflow generally, which meets the baseline but does not exceed it.

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 opens with a specific verb and resource—'Check a workflow and return every issue'—and goes further by specifying that issues are tagged by source ('schema' vs 'host'). Among siblings like get_workflow, run_workflow, and delete_workflow, only this one validates, and that uniqueness is clear without opening the schema.

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 explains how to interpret results and what action each issue source implies, but it never explicitly states when to use validate_workflow versus alternatives like run_workflow or get_workflow. Usage context is implied by the word 'validate' rather than stated, so while there is interpretational guidance after the call, there is no before-the-call when-to-use advice.

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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