CNAPS.ai MCP Server

run_flow

Execute a flow. It runs asynchronously — poll get_flow(view="status") for completion. For flows with input nodes, provide file URLs (from upload_file) via the inputs parameter. This runs the flow ONCE; use run_batch for multiple input sets.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`inputs`	No	JSON object mapping input node IDs to input arrays. Image/video/sound nodes: file URL arrays. Text nodes: text content arrays (auto-uploaded as text files). Example: {"1001": ["https://..."], "1002": ["A blue sky"]}
`flow_id`	Yes	The unique identifier of the flow to run.

TDQS

A4.6/5.0

Behavior4/5

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

The description discloses key behavioral traits: asynchronous execution, the need for polling, and the 'runs once' constraint. It also explains input file URL provenance from upload_file. This goes beyond annotations (readOnlyHint=false, idempotentHint=false) by clarifying execution semantics, though it does not detail side effects beyond running the flow.

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 serving a distinct purpose: execution, monitoring/inputs, and batch differentiation. The content is front-loaded with the core purpose and contains zero wasted words.

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?

Given its complexity (async, input handling, batch distinction), the description covers the essential operational aspects. It mentions polling and file URL requirements, and points to a sibling tool for status. Missing details like error handling are not critical given the polling instruction and no output schema.

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

Parameters4/5

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

Schema coverage is 100%, with both flow_id and inputs documented. The description adds value by explaining that inputs come from file URLs (from upload_file) and clarifying the 'runs once' behavior related to inputs. This supplements the schema's structural example without contradicting 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 'Execute a flow,' a specific verb+resource statement that clearly distinguishes it from sibling tools. It further differentiates from run_batch by explicitly stating 'This runs the flow ONCE,' making its scope immediately clear.

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

Usage Guidelines5/5

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

Provides explicit guidance on when to use this tool vs. alternatives: 'use run_batch for multiple input sets' names the alternative directly. It also instructs how to monitor execution via 'poll get_flow(view="status")' and how to handle input nodes, covering both selection and operation context.

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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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct resource and action, with clear separation between flow lifecycle, execution, model exploration, community, and account tools. Even similar-sounding tools like create_flow, preview_flow, and suggest_flow have clearly different purposes (actually creating, dry-running, and recommending models). Descriptions prevent misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (e.g., create_flow, list_flows, run_batch, cancel_flow). No mixed conventions or vague verbs like 'process' or 'handle'. The naming is uniform and predictable.

Tool Count3/5

At 33 tools, this is a large surface, but each tool addresses a distinct feature of the cnaps.ai platform, from flow CRUD and execution to community features and notifications. Still, it exceeds the typical well-scoped range and feels heavy, making it a borderline case between appropriate and too many.

Completeness3/5

The core flow lifecycle (create, read, update, delete, restore, duplicate) and execution (run, batch, cancel) are covered, but structural editing of flow graphs is missing—update_flow only changes parameters, not topology. Additionally, there is no run history, batch list/cancel, or community post update/delete, leaving notable gaps for a platform API.

Resources

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