Gemini Context MCP Server

Created By
MCP-Mirrora year ago
Mirror of
Overview

What is Gemini Context MCP Server?

Gemini Context MCP Server is a powerful implementation of the Model Context Protocol (MCP) that leverages Gemini's capabilities for context management and caching, maximizing the value of Gemini's 2M token context window.

How to use Gemini Context MCP Server?

To use the server, clone the repository, install dependencies, set up your environment variables with your Gemini API key, and start the server using Node.js.

Key features of Gemini Context MCP Server?

  • Up to 2M token context window support for extensive context capabilities.
  • Session-based conversations to maintain conversational state.
  • Smart context tracking with metadata for adding, retrieving, and searching context.
  • Semantic search for finding relevant context using similarity.
  • Automatic context cleanup for expired sessions and contexts.
  • Efficient caching of large prompts to optimize costs.

Use cases of Gemini Context MCP Server?

  1. Managing conversational AI sessions with context retention.
  2. Caching frequently used prompts to reduce token usage costs.
  3. Integrating with various MCP-compatible clients like Claude Desktop and VS Code.

FAQ from Gemini Context MCP Server?

  • What is the maximum context size supported?

The server supports a maximum context size of 2M tokens.

  • Is there a cost associated with using the Gemini API?

Yes, using the Gemini API may incur costs based on usage.

  • Can I integrate this server with other tools?

Yes, it can be integrated with various MCP-compatible clients.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
MCP-Mirror
Star
0
Language
TypeScript
License
-

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

2 days ago
Voyei

4 hours ago