Install
$ agentstack add mcp-srgrace-mcp-projects ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
mcp-projects
Open-Source Projects Repo for MCP (Model Context Protocol).
Steps to install and run:
- Clone this repo
- 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 ``
- 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) ```
- 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.
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Versions
- v0.1.0 Imported from the upstream source.