Fine-Tune AI Models Using MCP Servers.
GPT-4 costs $30 per 1M tokens for your classification task , fine-tune a $0.20/M model on Together AI that scores 96% accuracy, track every experiment in W&B, and save $29.80 per million tokens
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your agent identifies the task: 'Classify support tickets into 12 categories.' Step 1: Together AI launches a fine-tuning job on LLaMA-3-8B using your labeled dataset.
Step 2: W&B tracks the training run , loss curves, validation accuracy per epoch, hyperparameter configurations. Step 3: After training, the agent runs inference on your test set and compares against GPT-4 baseline.
Google Sheets gets the ROI report: 'Fine-tuned LLaMA-3-8B: 96.2% accuracy at $0.20/M tokens. GPT-4: 97.1% accuracy at $30/M tokens. Delta: -0.9% accuracy, -99.3% cost.
At 500K classifications/month: $15,000 $100. Annual savings: $178,800.'
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Together AI, Weights & Biases and Google Sheets so your AI agent launches fine-tuning jobs on Together AI, monitors training with W&B experiment tracking, and reports cost-accuracy comparisons in Sheets.
Together Ai
triggerLaunches fine-tuning jobs on open-source models and runs inference for evaluation
create_finetune_job list_finetune_jobs list_available_models chat_completion generate_embeddings Weights Biases
enrichmentTracks fine-tuning experiments , loss curves, hyperparameters, model versions and comparison across runs
list_wandb_projects list_project_runs get_run_details list_project_sweeps list_project_artifacts Google Sheets
actionCost-accuracy comparison dashboard , GPT-4 vs fine-tuned model ROI analysis
create_spreadsheet update_sheet_values append_sheet_values get_sheet_values Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Together Ai, Weights Biases & Google Sheets ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
AI engineers replacing expensive GPT-4 API calls with fine-tuned open-source models at 99% cost reduction
Startups tracking fine-tuning experiments with W&B to find the optimal accuracy-cost trade-off
ML teams building cost-accuracy comparison dashboards to justify fine-tuning ROI to leadership
AI enthusiasts learning model fine-tuning hands-on with managed infrastructure
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need?
Three: Together AI, Weights & Biases and Google Sheets.
Does this work with Claude Desktop?
Yes. Any MCP-compatible AI client works.
Do I need GPUs for fine-tuning?
No. Together AI manages all GPU infrastructure.
Is my training data secure?
MCP servers authenticate via API keys. Data stays in your Together AI and W&B accounts.
Benchmark Seed Valuations Using MCP Servers
Your portfolio valuations compared, market comps pulled, benchmark report built , know if $12M pre-money for a Seed is reasonable before you negotiate
Book Appointments via WhatsApp Using MCP
Your AI agent checks availability, sends time slots via WhatsApp and logs every booking
Build Serverless Data Warehouses Using MCP
You scrape data into CSV files that nobody queries , Firecrawl extracts structured web data, Neon stores it in serverless PostgreSQL you can query with SQL, and Sheets visualizes the results
Calculate Your Real Meeting Costs Using MCP
Your team has 340 hours of meetings this week across 47 events , and nobody has calculated that this costs $28,000 in engineering salaries just to sit in rooms and nod
Consolidate Scattered Knowledge Using MCP
Half your documentation is in Notion and half is in Coda because two teams chose different tools , now nobody can find anything and onboarding a new engineer takes 3 weeks instead of 3 days
Cut AI Model Costs Without Losing Quality via MCP
Your GPT-4o bill is $4,200/month and 60% of those calls could run on Groq for $0.003 , your agent finds the waste
MCP servers used in this workflow
Together AI
Together AI connects your local agent to dozens of open-source models and ML services. You can instantly generate chat completions, create vector embeddings for RAG pipelines, or fine-tune custom LLMs—all through one API endpoint. It lets you query Llama, Mixtral, and more from a single place without leaving your IDE.
Weights & Biases
Weights & Biases MCP Server lets you manage complex ML experiments through natural conversation. Instead of manually clicking through dashboards, your AI agent talks to your WandB account to list projects, monitor live runs, check hyperparameter sweeps, and pull full metrics for specific model versions (artifacts). It turns tedious dashboard navigation into direct chat queries.
Google Sheets
Google Sheets MCP Server lets your AI client read, write, and manage data directly in Google Sheets. Use conversational commands to pull data from specific ranges, append new rows, or structure entire spreadsheets. It acts as an analyst, letting you manipulate complex data without opening the GUI or writing formulas.