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doitintl

DoiT MCP Server

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by doitintl

build_cloud_flow

Destructive

Build a CloudFlow from a natural language description, generating nodes and connections while streaming incremental build events with the new flow's ID.

Instructions

Manage CloudFlow. Creates a new CloudFlow and generates its nodes and connections based on the provided natural language intent. The operation streams incremental build events, including the ID of the newly created flow, as they are produced.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesNatural language description of the CloudFlow to build from scratch.
conversationIdNoID of an existing conversation to continue. When omitted, a new conversation is started.
customerContextNoScope the request to a specific customer by ID. Required for DoiT employees (whose token isn't tied to a single customer); omit for direct customer users.
Behavior3/5

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

Annotations already indicate destructiveHint=true and readOnlyHint=false, so the description doesn't need to restate mutation. It adds value by mentioning streaming incremental build events and the flow ID, which is useful context beyond annotations. However, it doesn't detail what 'destructive' means (e.g., overwrites existing flows?) or other side effects, so it's moderate.

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 description is two sentences, front-loaded with the core purpose, and adds the streaming detail efficiently. No wasted words, though it could be slightly more structured with explicit usage guidance.

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

Completeness3/5

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

Given the tool's complexity (creates a flow with nodes/connections) and no output schema, the description provides the key outcome and streaming behavior. However, it doesn't mention prerequisites, error cases, or what happens to existing flows (destructive hint), leaving some gaps for a mutation 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?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds minimal extra meaning beyond the schema, only implying that 'question' is the main input. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool creates a new CloudFlow and generates nodes/connections from natural language intent, distinguishing it from sibling tools like trigger_cloud_flow, refine_cloudflow, and list_cloudflows. However, it doesn't explicitly contrast with these siblings, so it's clear but not fully differentiated.

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 implies usage for building a flow from scratch via natural language, but doesn't explicitly state when to use this vs. alternatives like refine_cloudflow or trigger_cloud_flow. No exclusions or alternative guidance is provided, so it's adequate but not explicit.

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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