Skip to main content
Glama
Buu-AI

Buu AI MCP Server

generate_model

Create models from image URLs and link them to specific subthreads using the Buu AI MCP Server tool designed for structured data processing.

Instructions

[PRIVATE] - Generate model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageRequestIdNoOptional ID of a previously generated image request
imageUrlYesImage URL used to generate the model
subthreadIdYesSubthread ID where the model will be linked

Implementation Reference

  • The async handler function for the 'generate_model' tool that sends a GraphQL mutation request using generateModelQuery and returns the response or error.
    async ({ imageRequestId, subthreadId, imageUrl }) => {
      try {
        const response = await client.request(generateModelQuery, {
          imageUrl,
          subthreadId,
          imageRequestId,
        });
        return { content: [{ type: 'text', text: JSON.stringify(response) }] };
      } catch (error) {
        console.error('Error calling generate_model:', error);
        return {
          isError: true,
          content: [
            {
              type: 'text',
              text: `Error: Failed to generate model. ${error}`,
            },
          ],
        };
      }
    }
  • Zod schema defining the input parameters for the 'generate_model' tool: imageRequestId (optional), imageUrl, subthreadId.
    {
      imageRequestId: z
        .string()
        .optional()
        .describe('Optional ID of a previously generated image request'),
      imageUrl: z.string().describe('Image URL used to generate the model'),
      subthreadId: z.string().describe('Subthread ID where the model will be linked'),
    },
  • Registration of the 'generate_model' tool on the McpServer, including name, description, input schema, and handler.
    server.tool(
      'generate_model',
      '[PRIVATE] - Generate model.',
      {
        imageRequestId: z
          .string()
          .optional()
          .describe('Optional ID of a previously generated image request'),
        imageUrl: z.string().describe('Image URL used to generate the model'),
        subthreadId: z.string().describe('Subthread ID where the model will be linked'),
      },
      async ({ imageRequestId, subthreadId, imageUrl }) => {
        try {
          const response = await client.request(generateModelQuery, {
            imageUrl,
            subthreadId,
            imageRequestId,
          });
          return { content: [{ type: 'text', text: JSON.stringify(response) }] };
        } catch (error) {
          console.error('Error calling generate_model:', error);
          return {
            isError: true,
            content: [
              {
                type: 'text',
                text: `Error: Failed to generate model. ${error}`,
              },
            ],
          };
        }
      }
    );
  • GraphQL mutation query definition 'generateModelQuery' used by the 'generate_model' tool handler.
    const generateModelQuery = gql`
      mutation GenerateModel($imageUrl: String!, $subthreadId: String!, $imageRequestId: String) {
        generateModel(imageUrl: $imageUrl, subthreadId: $subthreadId, imageRequestId: $imageRequestId) {
          ... on GenRequest {
            _id
            subthreadId
            teamId
            status
            metadata
            type
            images {
              alt
              keyS3
              size
              type
              url
            }
            model_mesh {
              alt
              keyS3
              size
              type
              url
            }
            timings {
              inference
            }
            credits
            createdAt
            updatedAt
            address
          }
          ... on HandledError {
            code
            message
          }
        }
      }
    `;
Behavior1/5

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 but fails completely. It doesn't indicate whether this is a read or write operation, what permissions might be required, whether it's destructive, what side effects occur, or what the typical response looks like. The '[PRIVATE]' hint suggests restricted access but doesn't explain what that means operationally.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely brief with only three words, which could be seen as concise. However, the '[PRIVATE]' prefix adds some structure by indicating access restrictions upfront. The brevity comes at the cost of meaningful information - it's under-specified rather than efficiently informative.

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

Completeness1/5

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

For a tool with 3 parameters, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what 'generating a model' means, what the inputs represent, what the expected output is, or how this tool relates to its siblings. The agent would struggle to understand when and how to use this tool effectively.

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 all parameters are documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema - no examples, no constraints, no relationships between parameters. This meets the baseline expectation when schema coverage is complete, but adds no extra value.

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

Purpose2/5

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

The description 'Generate model' is a tautology that merely restates the tool name without adding meaningful context. It doesn't specify what kind of model is being generated, what the generation process entails, or what resources are involved. The '[PRIVATE]' prefix suggests restricted access but doesn't clarify the tool's actual function beyond the name.

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

Usage Guidelines1/5

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

The description provides absolutely no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, appropriate contexts, or relationships to sibling tools like 'generate_image' or 'subthread_generate' that might serve similar purposes. The agent receives zero direction about appropriate usage scenarios.

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

Related 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/Buu-AI/buu-mcp-server'

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