Nebelus

Build an agent from a prompt

build_agent

AI-assisted build: describe the agent you want in plain language and the Nebelus Vibe Builder builds it for you — always as a DRAFT. Returns the created agent's editable surface plus the builder's assumptions. Unlike create_agent (which needs explicit fields), this SYNTHESISES the agent. Billed as AI credits at the build rate.

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": {
      "prompt": "<prompt>"
    },
    "name": "build_agent"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	Plain-language description of the agent to build.
`constraints`	No	Optional extra constraints to honor.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 14, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations are minimal (no read-only, destructive, or open-world hints implied), so the description carries meaningful weight. It discloses that the output is always a draft, that it returns the editable surface plus assumptions, and that it is billed as AI credits at the build rate. This adds useful behavioral context beyond the annotations, though it leaves out details like permissions or lifecycle implications.

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 three purposeful sentences with no filler. It front-loads the core purpose, then adds return behavior, differentiation from create_agent, and billing context, with each sentence contributing valuable information.

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?

The description is nearly complete for selecting and invoking the tool: it explains what input to provide, what output to expect, and how it differs from the main sibling. It does not detail error cases or exact response structure, but the lack of an output schema is partially mitigated by the explicit mention of the returned editable surface and assumptions.

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%, so the schema already documents both parameters clearly. The description reinforces that prompt is a plain-language request and mentions constraints, but it doesn't add meaningful new semantic detail beyond the schema definitions.

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 this tool builds an agent from a plain-language prompt, invoking a specific verb and resource. It explicitly differentiates itself from create_agent by noting the latter requires explicit fields and that build_agent synthesizes the agent.

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?

The description gives clear guidance on when to use this tool: when describing an agent in plain language with AI assistance. It names the alternative create_agent and contrasts the required input style, giving the agent enough context to choose correctly.

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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