Vexa
Meeting bot and transcripts for Google Meet, Teams and Zoom. Live or after, speakers labelled.
Description from the maker (original English)
MCP server for the OpenAI ChatGPT API — chat, vision, and embeddings
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Pick the AI app you use, then copy the settings.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 4 months ago.
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In the same category: Vexa, emailmd, MCP WhatsApp Cloud
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Meeting bot and transcripts for Google Meet, Teams and Zoom. Live or after, speakers labelled.
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An MCP (Model Context Protocol) server for the OpenAI ChatGPT API. Built in Rust, exposes chat completions, vision, embeddings, and model listing as MCP tools.
Communicates via stdio using JSON-RPC 2.0, like all MCP servers.
| Tool | Description |
|---|---|
chat | Send a chat completion request to ChatGPT with optional multi-turn history, system prompt, structured output (JSON schema), and model selection |
chat_with_vision | Analyse an image with ChatGPT's vision capabilities given an image URL and text prompt |
embedding | Generate text embeddings using OpenAI's embedding model |
list_models | List all available OpenAI models and their IDs |
Send a chat completion request. Supports multi-turn conversations via a JSON message history array, system prompts, structured output via JSON schema, temperature control, and model selection.
Parameters:
| Name | Type | Required | Description |
|---|---|---|---|
prompt | string | yes | The user message to send |
model | string | no | Model to use (default: gpt-4o) |
system_prompt | string | no | System prompt to set context |
messages | string | no | Full conversation history as JSON array of {role, content} objects |
temperature | float | no | Sampling temperature (0.0 - 2.0) |
max_tokens | integer | no | Maximum tokens to generate |
response_schema | string | no | JSON schema string to enforce structured output |
Analyse an image using ChatGPT's vision capabilities.
Parameters:
| Name | Type | Required | Description |
|---|---|---|---|
prompt | string | yes | Text prompt describing what to analyse |
image_url | string | yes | URL of the image (must be http:// or https://) |
model | string | no | Model to use (default: gpt-4o) |
detail | string | no | Image detail level: low or high (default: high) |
temperature | float | no | Sampling temperature (0.0 - 2.0) |
max_tokens | integer | no | Maximum tokens to generate |
Generate text embeddings.
Parameters:
| Name | Type | Required | Description |
|---|---|---|---|
input | string | yes | Text to embed as JSON: a single string or array of strings |
model | string | no | Embedding model to use (default: text-embedding-3-small) |
List all available OpenAI models. No parameters.
Create the config file:
mkdir -p ~/.config/mcp-server-gpt-chat
Create ~/.config/mcp-server-gpt-chat/config.toml:
api_key = "sk-..."
cargo build --release
This produces target/release/gpt-chat.
For development:
cargo build # debug build
cargo run # run in dev mode RUST_LOG=debug cargo run # run with debug logging
Add to your Claude Desktop config (~/.config/Claude/claude_desktop_config.json):
{
"mcpServers": { "gpt-chat": { "command": "/path/to/gpt-chat" } } }
src/
main.rs - entry point, config loading, stdio transport setup server.rs - MCP tool definitions (chat, chat_with_vision, embedding, list_models) api.rs - OpenAI HTTP client, request/response types, response formatters params.rs - tool parameter types with serde and JSON Schema derives config.rs - TOML config loading
MIT