Skip to main content
Glama

create_project

Create new web projects on AICre8 platform with customizable visibility settings (Public, Private, or Link access). Returns project ID and URL for immediate use.

Instructions

Create a new AICre8 project. Returns the project ID and URL ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoProject name (default: "API Project")
access_typeNoVisibility: Public (gallery), Private (owner only), Link (anyone with URL)

Implementation Reference

  • The MCP tool handler for 'create_project' that receives params, calls client.createProject(), and returns the result as JSON text content with error handling.
    async (params) => {
      try {
        const result = await client.createProject({
          name: params.name,
          access_type: params.access_type,
        });
        return {
          content: [
            {
              type: 'text' as const,
              text: JSON.stringify(result, null, 2),
            },
          ],
        };
      } catch (err: any) {
        return { content: [{ type: 'text' as const, text: `Error: ${err.message}` }], isError: true };
      }
    },
  • The createProject() method in AICre8Client that makes a POST request to '/projects' endpoint and returns the created project details (id, url_id, name, created_at, preview_url, access_type).
    async createProject(params: {
      name?: string;
      access_type?: 'Public' | 'Private' | 'Link';
    }): Promise<{
      id: string;
      url_id: string;
      name: string;
      created_at: string;
      preview_url: string | null;
      access_type: string;
    }> {
      return this.request('POST', '/projects', params);
    }
  • Zod schema definition for create_project tool input: 'name' (optional string) and 'access_type' (optional enum: Public, Private, Link).
    {
      name: z.string().optional().describe('Project name (default: "API Project")'),
      access_type: z
        .enum(['Public', 'Private', 'Link'])
        .optional()
        .describe('Visibility: Public (gallery), Private (owner only), Link (anyone with URL)'),
    },
  • src/index.ts:54-84 (registration)
    Registration of 'create_project' tool using server.tool() with name, description, Zod input schema, and async handler function.
    // ── Tool: create_project ──
    
    server.tool(
      'create_project',
      'Create a new AICre8 project. Returns the project ID and URL ID.',
      {
        name: z.string().optional().describe('Project name (default: "API Project")'),
        access_type: z
          .enum(['Public', 'Private', 'Link'])
          .optional()
          .describe('Visibility: Public (gallery), Private (owner only), Link (anyone with URL)'),
      },
      async (params) => {
        try {
          const result = await client.createProject({
            name: params.name,
            access_type: params.access_type,
          });
          return {
            content: [
              {
                type: 'text' as const,
                text: JSON.stringify(result, null, 2),
              },
            ],
          };
        } catch (err: any) {
          return { content: [{ type: 'text' as const, text: `Error: ${err.message}` }], isError: true };
        }
      },
    );
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool creates something new and returns specific data (project ID and URL ID), which is helpful. However, it doesn't mention permissions needed, whether creation is idempotent, rate limits, or error conditions - leaving significant behavioral gaps.

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 extremely concise - just two sentences that directly state the tool's purpose and return values. Every word earns its place with zero redundancy or unnecessary elaboration. It's front-loaded with the core action.

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?

For a creation tool with no annotations and no output schema, the description provides basic purpose and return information but lacks important context. It doesn't explain what happens after creation, error handling, or how the returned IDs should be used. The minimal information is adequate but leaves clear gaps for a mutation operation.

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 fully documents both parameters. The description adds no additional parameter information beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.

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 the specific action ('Create a new AICre8 project') and resource ('project'), distinguishing it from siblings like list_projects or deploy_project. It's not a tautology of the name and provides concrete information about what the tool does.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when not to use it, or compare it to sibling tools like list_projects or deploy_project. The agent must infer usage from context alone.

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

Install Server

Other Tools

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/AICre8dev/mcp-server'

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