The Logfire MCP Server is here! :tada:
| Publisher | pydantic |
| Repository | logfire-mcp |
| Language | Python |
| Forks | 26 |
| Stars | 161 |
| Available tools | 0 |
| Transport type | stdio |
| Categories | |
| License | MIT |
| Links |
Logfire exposes MCP capabilities that can be used by compatible AI clients and agents.
Browse the callable actions below, including names and descriptions when provided by the server.
Use the installation snippets to configure this server in your preferred MCP client.
161 stars and 26 forks from the linked repository.
[!IMPORTANT] This repository is archived. The STDIO MCP server in this package is no longer being updated. We now have a remote MCP server, which allows us to iterate faster on tools and provide a better experience.
Read more in our documentation.
If you have any questions, reach out to us on Slack or email us at engineering@pydantic.dev.
Node.js 18+
{
"mcpServers": {
"logfire": {
"command": "uvx",
"args": [
"logfire-mcp@latest"
],
"env": {
"LOGFIRE_READ_TOKEN": "YOUR_TOKEN"
}
}
}
}TypingMind connects MCP tools at the workspace level, so once Logfire is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.
Use this when the MCP server needs access to local files, apps, or private resources on your computer.
In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.
Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.
When the connector status is Ready, click Edit Servers and paste the MCP server configuration.
{
"mcpServers": {
"logfire": {
"command": "npx",
"args": [
"-y",
"logfire-mcp"
]
}
}
}Save the server list, open Plugins, enable the Logfire MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.
Logfire is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.
Yes. TypingMind connects MCP tools at the workspace level, so you can use Logfire with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.
TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Logfire connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.
Logfire runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.
Logfire exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.
No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Logfire requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.
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