July 30, 2025
Incerto vs MCP Servers - Why MCP servers just doesn't cut it for Database Management in Production.
While MCP (Model Context Protocol) servers enable LLMs to interact with databases through chat, they miss what makes a true database co-pilot effective.
If you're interested in a bullet point comparison, scroll down to the bottom of the page.
Let's explore what makes MCP servers fall short.
Security and Trust Risks
Are you really comfortable giving your production databases to unknown companies on Cursor and let it run on Production?
If its not a obvious strict no, then you should read this X thread.
AI should not have access to your production database directly in the first place. On top of that, most MCPs are subject to Inadequate AuthN/AuthZ, Prompt Injection & Tool Poisoning and code quality issues making them unsafe to use in production.
Performance and Scalability Concerns
MCP works well for simple, individual tool calls but struggles with more complex workflows that involve multiple steps, conditional logic, or human approvals. It doesn’t keep context across sessions, so external state management is needed.
MCPs also lacks support for multi-agent coordination or communication, workflow orchestration for multi-step reasoning, event-driven triggers, or autonomous decision-making. Building fully autonomous and collaborative agents requires adding custom orchestration layers on top.
Too many MCPs?
If you've ever gone down the rabbit hole of adding one MCP, you know you end up adding another one, and another one, and another one. One for clickhouse, one for postgres, one for grafana... list goes on.
Multiple MCPs don't really work properly together. A simple LLM is hit with cognitive overload (probability distribution), schema conflicts, and context loss, making it less effective.
Anything at production scale needs to be precise, fast and reliable. This is what Incerto is built for.
The Visual Understanding Gap
"The human brain processes visual information 60,000 times faster than text."
Getting all information as a big blob of text from a chat interface is not a good idea. Databases have dashboards, monitoring tools, Resource Utilization, Error Logs, Slow queries etc. Getting all this in a chat overwhelms the LLM & the user.
Visual Database Operations vs Text Output
Cost
LLMs are expensive. MCPs can give the right tools, but not the right direction.
LLM's can go unhinged if they take a wrong direction (which happens quite often), and it can end up generating a lot of tokens, completely missing the point. You pay not just for credits, but also for the time you spend correcting it.
The onus of guiding LLMs is on the user, which is neither trivial, nor fast.
Right Context
We've touched breifly upon how important direction and context is for a Co-Pilot for databases. DBMSes have so much context: TBs of data, 100s of queries running, lot of problems and errors firing, niche production problems. MCP servers are not designed to handle this.
"Incerto uses AI only when required and beneficial, with the right context and agency, and with database specific optimizations. Saving 30x in costs and achieving more than MCPs can."
To see how Incerto manages context and agency in detail, check out this blog.
Incerto is Production ready and Secure
Production has lot's of nuances, security being one of the top most. Incerto has lot of production crucial features like:
- Parallel task execution, across multiple hosts through AI.
- Deterministic, low latency production issue checks and alerts (with 1 click solutions)
- Your data never leaks, completely on your infra. Air gapped.
- 99.9999% uptime
- In production since January 2025
"Accidental deletes or updates are NOT possible with Incerto"
✅ Incerto vs ❌ MCP Servers
| Feature / Concern | Incerto ✅ | MCP Servers ❌ / ✅ |
|---|---|---|
| Secure for Production | ✅ Fully on your infra, air-gapped, no data leakage | ❌ Risky AuthN/AuthZ, prompt injection, data exposure risks |
| Built for Production Workflows | ✅ Parallel execution, deterministic checks & alerts | ❌ Limited to basic tool calls, no built-in workflow orchestration |
| Visual Interface for Operations | ✅ UI with dashboards, resource & query monitoring | ❌ Text-heavy chat only; lacks visual understanding |
| Cost Efficiency | ✅ Uses AI only when needed; 30x cheaper | ❌ Costly due to misdirected prompts and manual guidance |
| Context Awareness | ✅ Maintains rich DB context, handles long sessions | ❌ Stateless; context loss across sessions |
| Unified Tooling | ✅ One integrated co-pilot for all DB types | ❌ Requires multiple MCPs that don’t cooperate well |
| Production-Ready Reliability | ✅ 99.9999% uptime, no accidental updates/deletes | ❌ Not safe for critical environments without heavy customization |
| Scalable & Autonomous | ✅ Multi-agent reasoning, event triggers, human-in-loop | ❌ Lacks coordination, triggers, or conditional logic |
| Database-Specific Intelligence | ✅ Handles slow queries, schema changes, usage spikes | ❌ Generic LLMs not optimized for DB-specific patterns |
| Already Deployed in Production | ✅ Running live since Jan 2025 | ❌ Mostly used for prototyping, not real prod loads |
| Can Perform Basic DB Tasks | ✅ Yes | ✅ Good for simple queries and CRUD tasks |
| Easy to Set Up in Dev Environments | ✅ Yes | ✅ Lightweight and quick to try for local dev testing |
Incerto or MCP servers?
Once you use Incerto, you will never ask again!
Feel free to reach out with questions at LinkedIn.