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Mcp Projects

mcp-srgrace-mcp-projects · by SrGrace

My Projects Repo for MCP (Model Context Protocol)

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Install

$ agentstack add mcp-srgrace-mcp-projects

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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Declared compatibility

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Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

mcp-projects

Open-Source Projects Repo for MCP (Model Context Protocol).

Steps to install and run:

  1. Clone this repo
  2. Install the requirements

`` pip install mcp fastapi uvicorn fastapi-mcp llama-index llama-index-embeddings-huggingface llama-index-llms-langchain langchain-mcp-adapters mcp-use ``

  1. Make a .env file in the root folder with the following credentials:

``` APIKEY= PROJECTID= IBMCLOUDURL=

MODEL_ID=

TAVILYAPIKEY=

or, use your own llm provider - its agnostic to the projects (few changes needs to be done though) ```

  1. Experiment with different projects and files
  • make sure to run the mcp servers first and then only
  • run the clients

What is Model Context Protocol (MCP)?

At its core, MCP is a standardized way for applications to provide AI models with richer context about their environment, user preferences, and conversation history. Think of it as a smart, structured way to feed memory and context to AI systems.

The Problem MCP Solves

Current AI systems have limited "working memory" - they can only see a certain amount of conversation history at once (their "context window"). Imagine trying to have a conversation with someone who only remembers the last few exchanges:

  • You: "Remember that project we discussed last week about optimizing the supply chain?"
  • AI without good context: "I don't recall that specific discussion. Could you remind me of the details?"

This limitation forces users to constantly re-explain things, leading to frustrating interactions. MCP aims to solve this by creating a structured method for maintaining and accessing context.

Some Analogies

1. GPS Navigation

Traditional AI context management is like giving someone directions one turn at a time, without showing them the full map. If they forget a step, the journey breaks down.

MCP is like a GPS navigation system that:

  • Knows your destination
  • Remembers your preferred routes
  • Adjusts based on real-time conditions
  • Always knows exactly where you are in the journey

Read this medium article for comprehensive understanding of MCP: Understanding Model Context Protocol (MCP): A Layman’s Guide

Do make Pull Requests to contribute to this asset ✨

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

  • Author: SrGrace
  • Source: SrGrace/mcp-projects
  • License: MIT
  • Homepage: https://medium.com/@SrGrace_/understanding-model-context-protocol-mcp-a-laymans-guide-4737aab5fc6b

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.