Skip to main content
Glama

nimble

A token-efficient MCP server that lazy loads tools and proxies tool calls

  • comes with a local dashboard to connect and configure MCP servers

  • proxies tool calls with 3 simple top-level tools: list-tools, get-tool, execute-tool

  • supports tool summarization by LLM, you may also customize your own tool summaries

How it works

MCP clients naively include all tool descriptions and schemas on the context window. This results in excessive token consumption even when you only use a few tools. Multiply this over several MCP servers and your chat session becomes bloated. It's not only costly but also unsable as you'll quickly run into the model's token limit.

nimble solves the problem by allowing tools to be lazy loaded only when they're needed. The idea is connect to a unified MCP server with concise tool summaries and let LLM discover and expand full tool description when needed.

When tested with popular MCP servers (Notion, Linear, Figma, etc.), we see over 99% token savings on initial load and 90% during typical chat sessions.

Related MCP server: mcp-compressor

Installation

nimble runs over stdio. Configure your MCP client to launch it:

{
  "mcpServers": {
    "nimble-mcp": {
      "command": "npx",
      "args": ["-y", "nimble-mcp"],
      "env": {
        "NIMBLE_ENCRYPTION_KEY": "your-encryption-key",
        "NIMBLE_UI_PORT": "3333"
      }
    }
  }
}

The NIMBLE_UI_PORT determines the port for the local config server (default: http://localhost:3333).

The NIMBLE_ENCRYPTION_KEY is used to encrypt server credentials (access & refresh tokens), which are stored in local sqlite db.

Add OpenAI env vars here if you want LLM summaries to be automatically inferred when connecting a server.

Example:

{
  "mcpServers": {
    "nimble-mcp": {
      "command": "npx",
      "args": ["-y", "nimble-mcp"],
      "env": {
        "NIMBLE_ENCRYPTION_KEY": "your-encryption-key",
        "OPENAI_API_KEY": "sk-...",
        "OPENAI_MODEL": "gpt-5-mini"
      }
    }
  }
}

Tools

  • list-tools provides a list of available tools and a brief summary of each tool

  • get-tool retrieves a given tool's detailed description and schema

  • execute-tool performs the tool call that proxies the input argument to the original tool server

Quick guide

Once configuration in your MCP client complete, open the config dashboard (http://localhost:3333/) in the browser to setup MCP connections.

Add a server and authenticate

If you provided an OPENAI_API_KEY, the summaries will be automatically inferred by LLM (OpenAI for now). Otherwise, the first sentence from the description will be used. You may also customize this by clicking on the tool and modify the summary from the tool modal

You can also toggle tools on/off

Test out the server from the included MCP client

Repeat the process to add more MCP servers.

Development

Create a .env file in the repo root (see .env.example) to manage env vars locally.

npm run dev

Scripts

Server:

npm run build
npm run dev
npm test

UI:

npm run ui:build
npm run ui:dev
npm run ui:preview

Config UI

The server also hosts a local config UI on http://127.0.0.1:3333. The UI reads and writes the SQLite DB.

Build UI once:

npm run ui:build

Or build UI + server:

npm run build

Run UI dev server:

npm run ui:dev

LLM Summaries

Optional: auto-generate tool summaries on connect using OpenAI.

OPENAI_API_KEY=sk-... \
OPENAI_MODEL=gpt-5-mini \
npm run dev

Storage

nimble stores configuration and tool cache in a local SQLite database.

Default DB path:

./nimble.sqlite

Override with:

NIMBLE_DB_PATH=/path/to/nimble.sqlite

OAuth flow by URL/transport (manual):

NIMBLE_ENCRYPTION_KEY=your-encryption-key node dist/index.js --server-url https://mcp.notion.com/mcp --transport streamableHttp

If the server does not exist yet, this will auto-add a default OAuth entry and use a local callback at http://127.0.0.1:8787/callback.

Publish

npm login
npm publish --access public
A
license - permissive license
-
quality - not tested
D
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • A
    license
    -
    quality
    D
    maintenance
    A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.
    Last updated
    16
    10
    Apache 2.0
  • A
    license
    -
    quality
    A
    maintenance
    A proxy server that wraps existing MCP servers to significantly reduce token consumption by compressing tool descriptions into a two-step interface. It enables users to integrate extensive toolsets without exceeding context limits or incurring high API costs.
    Last updated
    104
    Apache 2.0
  • A
    license
    A
    quality
    D
    maintenance
    A proxy MCP server that reduces token usage by caching tool definitions locally and loading them on demand, supporting multiple backend MCP servers.
    Last updated
    5
    1
    Apache 2.0
  • F
    license
    -
    quality
    D
    maintenance
    An MCP server that reduces token usage by lazily loading skills and tools only when needed, and routing repetitive subtasks to ML backends instead of the LLM.
    Last updated

View all related MCP servers

Related MCP Connectors

  • A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…

  • Markdown-first MCP server for Notion API with 8 composite tools and 39 actions.

  • AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.

View all MCP Connectors

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/mquan/nimble'

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