Multi Capable Processing MCP Smart Agent

Created By
AdadAlShababa year ago
It is a modular and extensible AI agentic server system that connects specialized agents through a central REST API. These agents can analyze code repositories, fetch external data (like weather), generate text summaries, and remember past interactions using a persistent memory manager.
Overview

What is Multi-Capable Processing (MCP) Smart Agent?

Multi-Capable Processing (MCP) Smart Agent is a modular and extensible AI-driven server system that connects specialized agents through a central REST API. It can analyze code repositories, fetch external data like weather, generate text summaries, and remember past interactions using a persistent memory manager.

How to use MCP Smart Agent?

To use MCP Smart Agent, start the Flask server and interact with the API endpoints for various functionalities such as analyzing a GitHub repository, fetching weather data, or summarizing text.

Key features of MCP Smart Agent?

  • Multi-Agent Architecture for specialized tasks
  • Tool-Integrated Agents for code analysis and data fetching
  • Persistent Memory System for contextual recall
  • RESTful API for easy integration
  • Pythonic structure for extensibility and testing

Use cases of MCP Smart Agent?

  1. Analyzing GitHub repositories for insights
  2. Fetching real-time weather data
  3. Summarizing long texts for quick understanding

FAQ from MCP Smart Agent?

  • Can MCP Smart Agent analyze any GitHub repository?

Yes, it can analyze any public GitHub repository by providing the repository URL.

  • Is there a limit to the number of requests?

The current implementation does not enforce strict limits, but it is advisable to manage requests to avoid server overload.

  • How can I extend the functionality?

You can add new agents or tools by following the modular structure of the project.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
AdadAlShabab
Star
0
Language
Python
License
-

Recommend Servers

View All
AI Work Market — USDC settlement rails for AI labor on Base Mainnet)
@Dario (DME)

AI Work Market is a USDC escrow protocol on Base Mainnet, designed for autonomous AI agents to find work, post jobs, and settle payments without humans in the loop. This MCP server exposes 10 tools: **Escrow lifecycle** - `create_intent_quote` — get calldata + gas estimate for funding a new escrow intent - `submit_proof_quote` — get calldata for the seller to submit a proof URI - `release_funds_quote` — get calldata for the buyer to release payment (or claim/refund) **x402 single-call binding** - `x402_consume` — replaces the 5-step x402 flow with one HMAC-signed POST that returns a delivery URL **Onboarding & discovery** - `agent_onboard` — generate a signed agent card with marketplace attestation - `agent_search` — tf-idf search over the live agent catalog - `agent_reputation` — server-side reputation from on-chain Released/Refunded/Disputed events **Live state** - `system_status` — live on-chain state (nextIntentId, accumulatedFees, contract balance, owner) - `escrow_rules` — contract semantics, lifecycle, call guides, failure modes - `events_subscribe` — SSE stream of new on-chain intent events All endpoints are serverless (Vercel) and return their schema on GET. No browser, no wallet UI required for an agent to integrate. The protocol takes a 1% commission on every settlement; the rest goes to the seller. The full AgentCard is at `/.well-known/agent-card.json` (A2A-compatible). The OpenAPI 3.0.3 spec is at `/.well-known/openapi.json` with `components.securitySchemes` (none, hmacX402). `robots.txt` allows GPTBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, Amazonbot.

7 hours ago
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)

a day ago