Abhi Nexus Server

Author: Abhi
Built: June 2026
Stack: Python, FastMCP, Docker, Unraid, Ollama, VS Code

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What Is Abhi-Nexus?

Abhi-Nexus is a production-grade autonomous news intelligence system. It fetches articles from 8 tech sources every day, ranks them using AI, generates summaries in English and Hindi, and delivers a curated email digest at 8:15 AM — fully automatically, with zero human intervention.

It is engineered as a portfolio project that demonstrates real-world multi-agent architecture, production systems thinking, and cost-conscious data engineering.

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What Is MCP?

MCP (Model Context Protocol) is an open standard that gives LLMs the ability to interact with external systems in real time. Think of it as giving an AI hands — it can fetch data, trigger actions, and query systems beyond its training data.

Without MCP, an LLM can only think and talk. With MCP, it can act.

Real-world analogy: MCP is like a USB standard. Before MCP, every AI integration was a custom cable that only worked with one specific system. After MCP, it becomes a USB port — any compatible client can plug in and use it.

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What We Built

We exposed Abhi-Nexus as an MCP server — making the pipeline accessible to any MCP-compatible client (VS Code, Cursor, Open WebUI, or custom agents) without changing any existing agent code.

Architecture

MCP Client (VS Code + Continue + Local LLM)
                    |
         MCP Protocol (SSE, port 7070)
                    |
         Abhi-Nexus MCP Server
         (mcp_server.py — FastMCP 2.0.0)
                    |
    ┌───────────────┼───────────────┐
    ▼               ▼               ▼
Fetch Agent    Rank Agent     Delivery Agent
(RSS feeds)   (GPT scoring)   (Email SMTP)
    ▼               ▼               ▼
raw_articles   ranked_articles   Email at
   .json           .json          8:15 AM

Deployment Stack

Component	Technology
Server hardware	Unraid NAS
Containerization	Docker + docker-compose
Process management	Supervisord
MCP framework	FastMCP 2.0.0
Transport	SSE (Server-Sent Events)
MCP client	VS Code + Continue extension
Local LLM	Ollama (DeepSeek R1 14B, Llama 3)
Pipeline LLM	OpenAI GPT-4o-mini

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MCP Tools Exposed

Tool	What It Does
trigger_pipeline	Manually run the full Fetch → Rank → Email pipeline
get_articles	Fetch ranked articles with filters (category, score, limit)
get_top_articles	Quick shortcut for top N highest-scored articles
get_pipeline_status	Health check — last run time, article counts, errors
get_settings	View current pipeline settings (passwords masked)
update_setting	Change a setting without restarting the container
get_raw_articles	See Bronze layer unranked articles
scan_vulnerabilities	Scan packages against CVE database + detect hardcoded secrets in code
get_upgrade_recommendations	List outdated packages, prioritising security-critical ones

MCP Resources

Resource	URI	What It Exposes
Ranked articles	abhinexus://articles/ranked	Full ranked_articles.json as readable document
Pipeline status	abhinexus://status	Live pipeline health as readable document

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Security Scanning Tools

Two security tools were added to the MCP server using pip-audit — the same CVE database used by Snyk and Dependabot.

scan_vulnerabilities

Runs three real checks:

• Dependency CVE scan — checks every installed package against the NVD (National Vulnerability Database)
• Secret detection — scans all .py and .yml files for patterns matching OpenAI keys, GitHub tokens, AWS keys, hardcoded passwords
• Summary report — overall status: CLEAN or CRITICAL, with specific fix recommendations

Usage from VS Code Continue chat:

Scan Abhi-Nexus for vulnerabilities
Check for hardcoded secrets in the code
Run a full security audit

get_upgrade_recommendations

Lists all outdated packages with current vs latest version. Flags security-critical packages (fastapi, cryptography, openai, requests etc.) with HIGH priority above normal packages.

Installation

docker exec abhi-nexus pip install pip-audit

Add to requirements.txt:

pip-audit

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File Structure Added

z:/abhi-nexus/
└── mcp_server.py          ← New: MCP server exposing all 9 tools and 2 resources

Changes to Existing Files

File	Change	Reason
requirements.txt	Added fastmcp==2.0.0, pip-audit, upgraded openai==1.56.0, pydantic==2.13.4, uvicorn==0.49.0	Dependency resolution + security scanning
supervisord.conf	Added [program:mcp] block	Auto-start MCP server with container
docker-compose.yml	Added port 7070:7070, moved secrets to env vars	Expose MCP server, remove hardcoded secrets
Dockerfile	Added EXPOSE 7070	Document exposed port
agents/rank_agent.py	max_tokens 1500 → 6000, summary prompt shortened to 1 sentence	Fix JSON truncation bug
agents/fetch_agent.py	Added Unicode stripping in clean_text()	Remove garbage characters from Stack Overflow RSS

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Bugs Fixed During Build

Three pre-existing bugs were discovered and fixed during the MCP integration:

Bug 1 — OpenAI proxies Error

Symptom: Client.__init__() got an unexpected keyword argument 'proxies'
Cause: openai==1.30.0 was too old, incompatible with newer httpx
Fix: Upgraded to openai==1.56.0

Bug 2 — JSON Truncation in Rank Agent

Symptom: Unterminated string parse errors, all articles falling back to score 0
Cause: max_tokens=1500 was too low — GPT response cut off mid-JSON
Fix: Increased to max_tokens=6000, shortened summary prompt to 1 sentence

Bug 3 — Unicode Garbage Characters

Symptom: Stack Overflow articles had invisible Unicode characters (\u200b, \ufeff etc.) inflating token count
Cause: Stack Overflow RSS feed embeds tracking/watermark characters in content
Fix: Added character filter in fetch_agent.py clean_text() function

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How to Connect

VS Code (Continue Extension)

Add to ~/.continue/config.yaml:

name: Abhi-Nexus Config
version: 1.0.0
schema: v1

models:
  - name: DeepSeek 14B
    provider: ollama
    model: deepseek-r1:14b
    apiBase: http://localhost:11434

mcpServers:
  - name: abhi-nexus
    url: http://192.168.4.46:7070/sse

Cursor IDE

Add to Cursor MCP settings:

{
  "mcpServers": {
    "abhi-nexus": {
      "url": "http://192.168.4.46:7070/sse"
    }
  }
}

Verify Server is Running

curl http://192.168.4.46:7070/sse

Expected response:

event: endpoint
data: /messages/?session_id=...
: ping - 2026-06-13 ...

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Adding New Tools

Adding a new MCP tool is one decorated function:

@mcp.tool()
def my_new_tool(param: str = "default") -> dict:
    """Description of what this tool does."""
    # your logic here
    return {"result": "..."}

FastMCP automatically generates the schema, validates inputs, and registers the tool. No other changes needed.

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Interview Talking Points

What you built:

"I exposed a multi-agent autonomous news pipeline as an MCP server running on self-hosted Docker on Unraid. Any MCP-compatible client — VS Code, Cursor, or a custom agent — can now trigger the pipeline, query ranked articles, monitor health, and run security audits in real time. The LLM stays local using Ollama, so there is zero cloud dependency for the AI layer."

Why MCP over function calling:

"Function calling is per-model and per-request — you define tools inline for one specific app. MCP is transport-agnostic and model-agnostic. I build the server once and any MCP client can use it forever without rewriting the integration."

On security scanning:

"I added two security tools to the MCP server — one that runs pip-audit against the NVD CVE database to detect vulnerable packages, and one that scans the codebase for accidentally hardcoded secrets. The LLM can now ask 'is this system secure?' and get a real answer backed by real data."

Architecture decision — why not rewrite agents as MCP servers:

"The agents already work. MCP is an additive interface layer, not a replacement. I wrapped the existing FastAPI surface and Python functions as MCP tools without touching agent logic. This is the same pattern Meta uses — expose capabilities through a standard interface without disrupting the underlying system."

Cost discipline:

"The pipeline runs at $2/month. Batching 10 articles per API call instead of 1 saves 75% on GPT costs. The MCP server itself is free — open protocol, self-hosted, no per-query charges."

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What MCP Is Not

Misconception	Reality
MCP reads and understands your data like RAG	MCP is a messenger — it fetches or triggers, the LLM does the understanding
MCP requires Claude or Anthropic products	MCP is an open standard — works with any LLM and any compatible client
MCP replaces your existing API	MCP wraps your existing API — nothing gets replaced
Local LLMs work well for MCP tool calling	Small local models (under 30B) tend to hallucinate tool responses — larger models or cloud LLMs are more reliable

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Next Steps

• Upgrade to FastMCP 3.x for Streamable HTTP transport (replaces SSE)
• Add authentication layer to MCP server (API key header)
• Build a second agent that calls Abhi-Nexus as a tool in a larger workflow
• Add deep research tool — top 3 articles → web search → enriched summary
• Schedule automated weekly security scans via the MCP trigger