Skip to main content
Glama

emergent_create_project

Create a new project on Emergent.sh by submitting a detailed prompt. Receive a job ID to monitor the AI agent as it builds immediately.

Instructions

Create a new project on Emergent.sh by submitting a prompt. The AI agent will start building immediately. Returns a job ID you can use to monitor progress.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesFull instructions for what to build. Be specific — include design requirements, tech stack preferences, data structures, and any constraints.
modelNameNoAI model to use (default: claude-sonnet-4-5)

Schema Changelog

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

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses that the AI agent 'will start building immediately' (asynchronous kickoff) and that the return is a job ID rather than the completed project. This reveals important behavioral traits beyond the simple action of creating a project, such as the non-blocking nature of the operation.

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 short sentences, each earning its place: the first states the core action, the second explains immediate startup behavior, and the third covers the return value. The information is front-loaded and there is no redundancy or filler.

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 generally complete for a create-type tool: it covers the purpose, the async behavior, and the return value. It could optionally point to the specific sibling tool to monitor the job, but the job ID reference makes the next step inferable. Since the schema handles parameter details and no output schema exists, the description adequately covers what an agent needs to call this tool correctly.

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?

The input schema already provides detailed descriptions for both parameters: the 'prompt' description explains how to be specific, and 'modelName' includes an enum with a default. The tool description adds no additional parameter semantics beyond the phrase 'submitting a prompt,' so it stays at the baseline for full schema coverage.

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 states a specific verb ('Create'), a specific resource ('a new project on Emergent.sh'), and the mechanism ('by submitting a prompt'). This clearly distinguishes it from sibling tools like emergent_list_projects and emergent_get_project, which are read operations, and from wait_for_build/respond_to_agent, which handle later phases of a project lifecycle.

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 makes it obvious this tool is for starting a new build by submitting a prompt, and the sentence 'Returns a job ID you can use to monitor progress' implicitly routes the agent to later use tools like emergent_wait_for_build or emergent_get_project. It does not explicitly name these alternatives or provide when-not-to-use guidance, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Nextahalli/emergent-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server