Skip to main content
Glama
awkoy

replicate-flux-mcp

get_prediction

Retrieve detailed information about a specific prediction using its unique ID on the MCP server for Replicate Flux Model.

Instructions

Get details of a specific prediction by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
predictionIdYesID of the prediction to retrieve

Implementation Reference

  • Handler function that retrieves prediction details by ID from Replicate service and returns as formatted JSON text.
    export const registerGetPredictionTool = async ({
      predictionId,
    }: GetPredictionParams): Promise<CallToolResult> => {
      try {
        const prediction = await replicate.predictions.get(predictionId);
    
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(prediction, null, 2),
            },
          ],
        };
      } catch (error) {
        handleError(error);
      }
    };
  • Registration of the 'get_prediction' tool using server.tool, providing name, description, schema, and handler.
    server.tool(
      "get_prediction",
      "Get details of a specific prediction by ID",
      getPredictionSchema,
      registerGetPredictionTool
    );
  • Zod schema definition for get_prediction tool input (predictionId) and corresponding TypeScript type.
    export const getPredictionSchema = {
      predictionId: z.string().min(1).describe("ID of the prediction to retrieve"),
    };
    const getPredictionObjectSchema = z.object(getPredictionSchema);
    export type GetPredictionParams = z.infer<typeof getPredictionObjectSchema>;
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves details but doesn't describe what 'details' include (e.g., status, output, metadata), whether it's a read-only operation, error handling, or performance characteristics. This leaves significant gaps for an agent to understand the tool's behavior.

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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and uses clear language. Every word earns its place, making it easy for an agent to parse quickly.

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

Completeness2/5

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

Given the tool's complexity (retrieving a specific resource) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' are returned, error conditions, or how this fits into broader workflows with siblings. For a tool with no structured output information, more context is needed.

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 schema description coverage is 100%, with the parameter 'predictionId' clearly documented in the schema. The description adds minimal value beyond the schema by implying the parameter is required ('by ID') but doesn't provide additional context like format examples or constraints. 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's purpose with a specific verb ('Get') and resource ('prediction'), and identifies the key input ('by ID'). It distinguishes from siblings like 'prediction_list' (which retrieves multiple predictions) by focusing on a single prediction. However, it doesn't specify what 'details' include, leaving some ambiguity.

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 when to choose 'get_prediction' over 'prediction_list' (for listing multiple predictions) or other siblings like 'create_prediction'. There's no context about prerequisites, such as needing an existing prediction ID.

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/awkoy/replicate-flux-mcp'

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