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TypeScript MCP Server Boilerplate

TypeScript MCP Server Boilerplate

by kimhailey
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

generate-image

Generates images from text prompts using the FLUX.1-schnell model. Specify inference steps for desired quality.

Instructions

HuggingFace FLUX.1-schnell 모델로 이미지를 생성합니다. HF_TOKEN 환경변수가 필요합니다.

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": "generate-image"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYes이미지 생성 프롬프트
num_inference_stepsNo추론 스텝 수 (1~10, 기본값 4)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description bears full burden. It discloses the model and env requirement, but omits details like error behavior if token is missing, rate limits, or output format. The added context is moderate.

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

Conciseness4/5

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

The description is a single sentence, front-loaded with key information (model and env requirement). No fluff, but could potentially add more value without sacrificing conciseness.

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?

Given the low parameter count and full schema coverage, the description is fairly complete but lacks information about output (no output schema) and error handling. It adequately covers prerequisites but falls short on behavioral details.

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% with descriptions for both parameters (prompt, num_inference_steps). The description adds no extra semantic information beyond the schema, so baseline 3 is appropriate.

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 explicitly states that the tool generates an image using the HuggingFace FLUX.1-schnell model, clearly identifying the verb (generate) and resource (image/model). Sibling tools are unrelated (calculate, geocode, etc.), so no ambiguity.

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

Usage Guidelines3/5

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

The description mentions the required HF_TOKEN environment variable but provides no guidance on when to use this tool versus alternatives, nor when not to use it. Since siblings are not image-generation tools, usage is implicitly clear but lacks explicit direction.

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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