5 MCP servers every engineering team should run in 2026

If you’re not running any MCP servers yet, you’re still copy-pasting context into every AI session like it’s 2024.

MCP (Model Context Protocol) is dead simple: servers expose your data and tools through a unified interface, and hosts (Claude, Cursor, your agents) consume them. One protocol, any source.

There are thousands of MCP servers out there now. Most of them don’t matter. These five do, and I’d set them up roughly in this order.

The stack

graph TD
  AI["Your AI Tools"] -->|MCP| FS["1. Filesystem"]
  AI -->|MCP| GH["2. GitHub"]
  AI -->|MCP| DB["3. Database"]
  AI -->|MCP| OB["4. Observability"]
  AI -->|MCP| MM["5. Memory Layer"]
  FS --- FSD["Code, configs, local files"]
  GH --- GHD["PRs, issues, code review"]
  DB --- DBD["Schema, queries, data"]
  OB --- OBD["Logs, metrics, traces"]
  MM --- MMD["Decisions, patterns, history"]
  style AI fill:#1e1e3a,stroke:#6366f1,color:#e2e8f0
  style MM fill:#1e1e3a,stroke:#22c55e,color:#e2e8f0

1. Filesystem server

Gives AI tools direct, scoped access to your local files: read, write, search, navigate.

Without this, every conversation starts with you copy-pasting code into a chat box. With it, the AI reads the actual file, sees the full module structure, and can write changes back. Completely different experience.

Most tools handle this natively now. Claude Desktop has @files, Cursor indexes your codebase automatically. If you need explicit control, use the standalone server. Just scope it to the project root. I’ve seen people expose their entire home directory. Don’t.

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"]
    }
  }
}

2. GitHub server

Connects AI tools to GitHub: repos, PRs, issues, review comments, CI status, commit history.

Code doesn’t exist in isolation. When you’re debugging, the AI should be able to see the PR discussion, the linked issue, whether CI passed last time. Without this, you’re alt-tabbing between GitHub and your AI tool, pasting URLs.

Here’s the kind of thing that changes: you ask Claude to help refactor a module. It checks if someone already has an open PR touching the same files, reads the review comments from the last attempt, and avoids the exact mistakes that got flagged before. That’s context you wouldn’t bother providing manually.

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "<your-token>" }
    }
  }
}

3. Database server

Lets AI tools inspect your schema, run read-only queries, and understand your data model without you explaining table relationships.

Half of engineering questions involve data. “What’s the relationship between users and organizations?” “Why is this query slow?” “What does the status field actually contain?” Instead of you explaining the schema, the AI just looks. Servers exist for Postgres, MySQL, SQLite, MongoDB, and most others. Pick the one matching your stack.

One non-negotiable: read-only credentials. Always. No exceptions. Set a query timeout, and seriously consider connecting to a staging replica instead of production.

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": { "DATABASE_URL": "postgresql://readonly:***@staging:5432/myapp" }
    }
  }
}

4. Observability server

Connects AI tools to your logs, metrics, and traces (Datadog, Grafana, whatever you use).

Debugging with AI is already useful. Debugging with AI that can see your actual logs is something else entirely. Instead of “paste the error,” it searches recent logs for the exception, correlates it with a deployment, and checks whether latency spiked at the same time.

Say you’re investigating a timeout in the payment service. The AI queries your observability stack, spots a P99 spike right after Friday’s deploy, traces it to a specific endpoint, and points you to the commit. That investigation used to take an hour of clicking through dashboards.

graph LR
  Q["Engineer asks: why is payments slow?"] --> A["AI queries observability MCP"]
  A --> L["Finds latency spike at 3pm Friday"]
  L --> D["Correlates with deploy #847"]
  D --> C["Identifies commit abc123"]
  C --> R["Suggests rollback or fix"]
  style Q fill:#1e1e3a,stroke:#6366f1,color:#e2e8f0
  style R fill:#1e1e3a,stroke:#22c55e,color:#e2e8f0

Fair warning: the observability MCP ecosystem is younger than the others. You’ll probably find a community server for Datadog or Grafana, but for less common stacks you may need to wrap your monitoring tool’s API yourself. The good news is that these are usually straightforward: a few endpoints, read-only.

5. Memory layer

Gives AI tools access to persistent, shared knowledge: architectural decisions, team conventions, past solutions, the kind of institutional context that doesn’t live in code or docs.

I put this last but I think about it first. Servers 1-4 connect AI to what your systems are. The memory layer connects AI to what your team knows. Different kind of context entirely.

Your filesystem shows the current code. GitHub shows the history. The database shows the data. Observability shows the runtime behavior. But none of them capture:

• Why the code is structured this way
• Who decided to use this pattern and when
• What was tried before and didn’t work
• How this service relates to the team’s broader architecture

graph TD
  subgraph IN["What MCP servers 1-4 expose"]
      F["Current code"]
      G["Git history"]
      D["Database schema"]
      O["Runtime metrics"]
  end
  subgraph OUT["What only a memory layer captures"]
      W["Why decisions were made"]
      P["Patterns that work"]
      H["Past solutions to similar problems"]
      T["Team conventions and preferences"]
  end
  IN -.- GAP["The knowledge gap"]
  GAP -.- OUT
  style GAP fill:#1e1e3a,stroke:#ef4444,color:#ef4444
  style OUT fill:#1e1e3a,stroke:#22c55e,color:#e2e8f0

This is the gap between an AI tool that can read your code and one that understands your codebase.

What I’d look for in a memory server: graph + vector hybrid so you get relationships between facts rather than just semantic similarity. Automatic updates that stay current without someone maintaining them (this rules out most wikis). Team-scoped so everyone’s AI tools share the same context, not just yours. And provenance, so you can trace where every piece of knowledge came from.

Several tools offer MCP memory servers today. Mem0, Cognee, and Zep all have one. We compared them in our AI Memory Landscape.

The order matters

I’d set these up in order. Each one builds on the previous:

With all five running, your AI tools stop feeling like smart autocomplete and start feeling like a teammate who’s been around for six months. They know the code, the context, the conventions, and why things ended up the way they are.

One protocol, one setup

The nice thing about MCP: this isn’t five separate integrations with five different APIs. It’s one protocol. Add a server to your config, restart your tool, done. When a better server comes along for any of these categories, swap it out. Nothing else changes.

You invest in the setup once. The ecosystem improves around you.