The Tool Desk
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Short answer: build an MCP server that exposes a narrowly defined generate_image tool, validate the tool arguments, call an image-generation API from the handler, and return the resulting image or a retrievable reference to the MCP client. MCP is the connection protocol; it is not an image model or an image-generation service.
Start with a local stdio server while you develop. Move to HTTP for an already-running service, or stable HTTPS with Streamable HTTP when clients must reach a deployed server. Keep provider credentials in the server environment, inspect every tool call with MCP Inspector, and add authentication before exposing the endpoint.
What the architecture contains
An MCP integration has three distinct parts:
- Client: an MCP-compatible host such as an agent or desktop application. It initializes the connection, discovers tools, and sends structured arguments.
- Server: your process or hosted service. It publishes tool names, descriptions, input schemas and handlers.
- Image provider: the API or service that actually creates pixels. The server calls it and translates its response into MCP content.
The client never needs the provider’s secret key. It only sees the tool contract and the result. Keep the tool focused: image generation, editing, or variation can be separate tools when their inputs and permissions differ.
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The official SDKs documented for this setup are the TypeScript package @modelcontextprotocol/sdk and the Python package mcp. Use the language already used by your project, deployment and logging stack. Then select an image provider and read its current request, model, size, quality, output-format and safety documentation; those parameters change, and MCP does not standardize them.
The example below uses TypeScript and a configurable provider HTTP endpoint. It demonstrates the MCP wiring without pretending that one request schema works for every image service. Replace the provider payload and response parsing with the exact current contract of your chosen API.
Build a focused TypeScript server
1. Create the project
mkdir image-mcp && cd image-mcpnpm init -ynpm install @modelcontextprotocol/sdknpm install -D typescript tsx @types/node
Set your package to use ESM and add a script such as "start": "tsx server.ts". Store secrets outside source control:
export IMAGE_PROVIDER_URL="https://your-provider.example/generate"
export IMAGE_PROVIDER_KEY="replace-me"The variable names above are yours to define. A package’s documented variable, such as OPENAI_API_KEY, applies only when you use that package’s implementation; do not assume it is universal.
2. Implement the server and tool
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
const providerUrl = process.env.IMAGE_PROVIDER_URL;
const providerKey = process.env.IMAGE_PROVIDER_KEY;
if (!providerUrl || !providerKey) {
throw new Error("Set IMAGE_PROVIDER_URL and IMAGE_PROVIDER_KEY");
}
const server = new McpServer({ name: "image-generation", version: "1.0.0" });
server.tool(
"generate_image",
"Create one image from a user prompt. Use only for image-generation requests.",
{
prompt: z.string().min(1).max(4000),
size: z.enum(["small", "medium", "large"]).optional(),
format: z.enum(["png", "jpeg", "webp"]).optional()
},
async ({ prompt, size = "medium", format = "png" }) => {
const response = await fetch(providerUrl, {
method: "POST",
headers: {
"content-type": "application/json",
"authorization": `Bearer ${providerKey}`
},
body: JSON.stringify({ prompt, size, format })
});
if (!response.ok) {
const detail = await response.text();
throw new Error(`Image provider returned ${response.status}: ${detail}`);
}
const result = await response.json() as {
image_url?: string;
image_base64?: string;
revised_prompt?: string;
};
if (result.image_url) {
return {
content: [
{ type: "text", text: result.revised_prompt
? `Generated image. Revised prompt: ${result.revised_prompt}`
: "Generated image." },
{ type: "image", data: result.image_url, mimeType: `image/${format}` }
]
};
}
if (result.image_base64) {
return {
content: [
{ type: "text", text: "Generated image." },
{ type: "image", data: result.image_base64, mimeType: `image/${format}` }
]
};
}
throw new Error("Provider response contained neither image_url nor image_base64");
}
);
const transport = new StdioServerTransport();
await server.connect(transport);This is runnable once the provider endpoint accepts the illustrated request and returns one of the illustrated response fields. For a real provider, change only the provider call and response mapping to match its current documentation. Never return the API key, raw authorization headers or private provider diagnostics in tool content.
3. Make the schema useful to the model
Descriptions are part of the interface. State when the tool should be called, what the prompt means, allowed sizes and output formats, and any limits such as maximum prompt length. Reject empty prompts, unknown enum values and oversized input before making a paid provider request. Keep safety checks in the handler as well as in the client-facing description.
Return images safely and usefully
An MCP result can contain text plus image content, or text containing a URL or job identifier that the client can retrieve. Inline base64 is simple for small outputs but increases message size. A short-lived signed URL is more efficient for large files, provided it does not expose private assets publicly. Do not put secrets in URLs, prompts, logs or result text.
Decide how failures are represented. Authentication failures, invalid arguments, provider quota errors, content-policy refusals and timeouts should be distinguishable in server logs while exposing a concise, actionable message to the client. Set request timeouts and cancel upstream work when the MCP call is cancelled.
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Choose stdio, HTTP or Streamable HTTP
| Transport | Best fit | Reachability and trade-off |
|---|---|---|
| stdio | Local development or a client-launched process | The client starts the process; no public listener is required. Process lifetime and local environment management are your responsibility. |
| HTTP | An already-running service on a private or shared network | Clients connect to an endpoint. You must operate the process, authentication and network access. |
| HTTPS with Streamable HTTP | Public production deployment | Use a stable HTTPS URL, enforce authorization and monitor failed initialization and tool calls. |
Transport support differs by client. Confirm the target host’s connection settings before choosing a deployment model. A local-only workflow can remain on stdio; cloud hosting is not required just to build or inspect the server.
Connect a client
Local stdio
Configure the client with the command that starts your server, its working directory and the environment variables it needs. The client launches the process, performs MCP initialization, then requests the tool list. Do not place provider keys in a client prompt or checked-in client configuration.
Running HTTP service
Run the server under a process manager, bind it to the intended interface, and give the client the exact endpoint and authorization method it supports. Restrict origins and network access where possible. Test initialization from the same network location as the client, not only from localhost.
Private OpenAI connections
For supported OpenAI products, Secure MCP Tunnel can provide an outbound-only route to a private server without opening a public listener. It is a connection method, not public plugin hosting. Public submission still requires a stable, reachable HTTPS MCP endpoint, and availability depends on the target product.
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Use MCP Inspector for local Streamable HTTP inspection, as recommended in the build guidance. Work through this checklist:
- Confirm initialization completes and the server reports the expected name and version.
- Inspect the tool name, description, schema, required fields and enum values.
- Call a valid prompt and verify the returned image content or retrieval reference.
- Try empty, oversized, malformed and out-of-range arguments; confirm they fail before provider billing.
- Simulate provider timeouts, 4xx responses, 5xx responses and malformed JSON.
- Verify annotations, authorization checks and that secrets never appear in results or logs.
- In the target client, test direct requests, indirect requests, edge cases and requests outside the tool’s scope.
Credentials, safety and operations
- Load provider credentials from the runtime secret store or environment and rotate them without changing tool schemas.
- Apply authentication and authorization to every remote endpoint. A tool that can spend money or create regulated content is an action, not an anonymous convenience.
- Limit prompt length, output size, concurrency and total provider spend. Add retries only for transient failures and use exponential backoff.
- Log request IDs, latency, status and failure category, but redact prompts when they may contain personal or confidential data.
- Cache only when the prompt, options and privacy policy permit it. Do not treat generated URLs as permanent unless the provider guarantees that.
Common failures and fixes
The client cannot initialize
Check the command path, working directory, executable permissions and environment variables for stdio. For HTTP, verify DNS, TLS, firewall rules, endpoint path and the client’s supported transport.
The tool does not appear
Inspect the server’s registered tool name and startup logs. A crash before registration, an invalid schema or connecting to the wrong endpoint prevents discovery.
The provider returns unauthorized
Confirm the server process received the key, the authorization header format matches the provider’s current documentation, and the key has access to the selected model or operation.
Images are missing from results
Compare the provider response with your parser. It may return base64, a temporary URL, a job ID or a different nesting structure. Map that format explicitly and set the correct MIME type.
Best Value
Requests time out
Increase the client and upstream timeouts only as needed, pass cancellation through, and consider an asynchronous job tool for providers whose generation routinely exceeds interactive limits.
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Use the API examples in the ScreenshotNeo documentation:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
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Frequently Asked Questions
Does MCP generate images by itself?
No. MCP defines discovery, schemas, transport and results; your tool handler must call an image-generation provider.
Can I keep an image-generation MCP server completely local?
Yes. A client-launched stdio process can remain local, provided the provider API is reachable from that process and its credentials stay in the local runtime.
When should generation be asynchronous?
Use an asynchronous design when provider jobs exceed interactive timeouts or when you need progress, retries and durable result storage.
Quick Recap
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