Best MCP Servers for Backend Developers in 2026

Backend development is fundamentally about managing systems that talk to other systems. Your API talks to the database. The database feeds the cache. The cache invalidates when the queue processes a message. Containers orchestrate the whole thing. And when something breaks at 2 AM, you are digging through error monitoring dashboards trying to correlate a stack trace with the deployment that caused it.

The friction is not in writing the code -- it is in the constant context-switching between your editor, the database client, the Docker dashboard, the GitHub PR interface, and the Sentry error feed. Model Context Protocol (MCP) servers eliminate these context switches by bringing the systems you manage directly into your AI coding assistant's reach.

This guide covers five MCP servers that address the core backend workflow: database access, container management, code collaboration, error monitoring, and caching.

PostgreSQL MCP -- Your Database, Inside Your Editor

Author: Anthropic | Tools: 8 | Setup: Connection string in args

Every backend developer spends a significant portion of their day interacting with the database. Writing queries, inspecting schemas, debugging slow queries, migrating tables. PostgreSQL MCP brings all of this into your coding session by connecting your AI assistant directly to your Postgres instance.

What It Does

The server exposes eight tools that cover the essential database operations: running SQL queries, inspecting table schemas, listing tables across schemas, and examining indexes and constraints. Your assistant can read the actual structure of your database -- not a stale ERD diagram from three sprints ago, but the live schema with all its columns, types, and relationships.

This is particularly valuable because the assistant can correlate what it sees in your application code with the actual database schema. If your ORM model has a field called created_at but the column is actually creation_date, the assistant catches that mismatch because it can read both your code and your schema in the same conversation.

How It Helps in Practice

You are implementing a new API endpoint that needs to join data across three tables. Instead of opening pgAdmin, writing a test query, tweaking it until it returns the right results, and then translating it into your ORM layer, you describe what data you need. The assistant inspects the schema, understands the relationships, writes the query, tests it against the database, and then generates the corresponding ORM code -- all without you leaving your editor.

Or you are investigating a performance issue. A particular endpoint is slow and you suspect the database query is the bottleneck. The assistant can run an EXPLAIN ANALYZE on the query, read the execution plan, identify the sequential scan that should be an index scan, and suggest the specific index to create. It can even create the migration for it.

Schema migrations are another area where this server shines. The assistant can inspect the current schema, compare it against your desired changes, generate the migration SQL, and run it -- all within the same conversation where you discussed the feature requirements.

Configuration

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://user:password@localhost:5432/mydb"
      ]
    }
  }
}

Replace the connection string with your actual database URL. For security, point this at a development or staging database, not production.

Docker MCP -- Container Management Without the Dashboard

Author: Community | Tools: 14 | Setup: Zero-config (npx)

Backend applications almost always run in containers. Whether it is your local development environment, your test suite, or your staging deployment, Docker is the layer between your code and the runtime. Docker MCP gives your assistant full control over your Docker environment: starting containers, stopping them, inspecting logs, managing images, and troubleshooting networking issues.

What It Does

The server provides fourteen tools covering the full Docker lifecycle. Your assistant can list running containers, start and stop containers, pull and build images, manage volumes and networks, inspect container details, read container logs, and execute commands inside running containers. It sees the same information you would get from docker ps, docker logs, and docker inspect, but it can act on it within the context of your coding conversation.

How It Helps in Practice

You are debugging an issue where your API cannot connect to the database. The error says "connection refused." Instead of opening a terminal, running docker ps to check if the database container is running, docker logs db to see if it started correctly, and docker network inspect to verify the containers are on the same network, you ask your assistant to diagnose the problem. It checks the container status, reads the logs, inspects the network configuration, and tells you that the database container started but crashed because the volume mount path was wrong.

Another common scenario: you need to spin up a local stack with multiple services for testing. The assistant can create the necessary containers, configure the network, set environment variables, and verify everything is running. When you are done, it tears down the whole stack.

Docker MCP is also useful for maintaining Dockerfiles. The assistant can compare your Dockerfile against the running container, identify unnecessary layers or missing optimizations, and suggest improvements based on the actual build output.

Configuration

{
  "mcpServers": {
    "docker": {
      "command": "npx",
      "args": ["-y", "docker-mcp"]
    }
  }
}

No API keys required. The server communicates with your local Docker daemon. Make sure Docker Desktop or the Docker engine is running.

GitHub MCP -- Code Review and PR Management in Your Editor

Author: Anthropic | Tools: 20 | Requires: GitHub personal access token

Code review is one of the most context-heavy activities in backend development. You need to understand the PR's intent, read the code changes, check the tests, verify the CI status, and write meaningful feedback. GitHub MCP connects your assistant to the GitHub API, letting it read PRs, review diffs, manage issues, and interact with your repository without you opening a browser tab.

What It Does

With twenty tools, this is one of the most comprehensive MCP servers available. Your assistant can list and read pull requests, review diffs, create and manage issues, check workflow run statuses, manage branches, search code across repositories, read file contents from any branch, and create or update pull requests. It covers the full GitHub workflow from issue creation through code review to merge.

How It Helps in Practice

A teammate opens a PR that touches your API's authentication layer. Instead of switching to the GitHub UI, scrolling through the diff, opening individual files for context, and cross-referencing the changes with the existing codebase, you ask your assistant to review it. It pulls the PR diff, reads the modified files, checks the related tests, and provides a structured review: what the changes do, whether they introduce any security concerns, whether the error handling is consistent with the rest of the codebase, and whether the tests cover the edge cases.

For your own PRs, the assistant can create the pull request directly, write the description based on the commits, link related issues, and even suggest reviewers based on the files changed.

Issue triage is another strong use case. The assistant can read new issues, categorize them based on the description and labels, and draft initial responses or link them to relevant code. This is particularly useful when you are maintaining an open-source project alongside your day job.

Configuration

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

Generate a personal access token from your GitHub settings. Grant it repo, issues, and workflow permissions as needed.

Sentry MCP -- Error Monitoring That Feeds Into Your Editor

Author: Sentry | Tools: 8 | Requires: Sentry auth token

When something breaks in production, the typical backend developer workflow is: get an alert, open Sentry, find the error, read the stack trace, switch to your editor, find the relevant code, understand the context, write a fix, and create a PR. That is at least four context switches before you even start fixing the problem.

Sentry MCP collapses this workflow by letting your assistant query Sentry directly. It can read error details, stack traces, affected users, frequency data, and performance metrics -- all within the same conversation where you are writing the fix.

What It Does

The server provides eight tools for interacting with Sentry's error tracking and performance monitoring. Your assistant can list recent issues, read detailed error information including stack traces, query error frequency and trend data, check affected user counts, and access performance transaction data. It sees the same information available in the Sentry dashboard, but in a format the assistant can reason about alongside your code.

How It Helps in Practice

You get a spike in 500 errors on your API's payment processing endpoint. Instead of opening Sentry, finding the error group, reading through the stack traces, and trying to correlate it with recent deployments, you ask your assistant what is happening. It queries Sentry, finds the error group, reads the stack trace, and tells you that a NullPointerException is occurring in the webhook handler because a new payment provider returns a different payload format than expected.

The assistant can then look at the relevant code, suggest a fix that handles the new payload format, and even check whether the fix would also address the older, lower-frequency variant of the same error that has been lingering for weeks.

Performance monitoring through Sentry MCP is equally useful. The assistant can query slow transactions, identify which endpoints have degraded recently, and correlate the timing with specific code changes. It bridges the gap between "something is slow" and "here is the exact database query that is causing the latency spike."

Configuration

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": ["-y", "sentry-mcp"],
      "env": {
        "SENTRY_AUTH_TOKEN": "your-sentry-token"
      }
    }
  }
}

You will need an auth token from your Sentry account settings. The token needs read access to your projects and issues.

Redis MCP -- Cache Inspection and Management

Author: Community | Tools: 8 | Requires: Redis connection URL

Caching is critical infrastructure for any backend that handles real traffic. But caches are also a common source of bugs: stale data, missing keys, serialization issues, TTL misconfigurations. Redis MCP gives your assistant direct access to your Redis instance so it can inspect keys, check values, monitor memory usage, and debug caching behavior alongside your application code.

What It Does

The server provides eight tools for interacting with Redis: getting and setting keys, listing keys by pattern, checking TTLs, deleting keys, and running arbitrary Redis commands. Your assistant can see exactly what is stored in the cache, how long it has been there, and whether the values match what your application expects.

How It Helps in Practice

Your API is returning stale user profile data even though the database has been updated. You suspect the cache is not being invalidated correctly. Instead of opening a Redis CLI, running KEYS user:*, checking the TTL on each key, and manually comparing the cached data with the database, you ask your assistant to investigate. It queries both the Redis cache and the Postgres database (using the Postgres MCP server), compares the values, identifies the stale keys, and traces the issue back to a missing cache invalidation call in your update handler.

Another scenario: you are implementing a new rate-limiting system using Redis sorted sets. The assistant can write the application code, set up the Redis data structures, test them with sample data, and verify that the rate limiting behaves correctly -- all within one conversation. It can read the Redis state to confirm that keys are being created with the right TTLs and that expired entries are being cleaned up properly.

Redis MCP is also useful for monitoring. The assistant can check memory usage patterns, identify keys that are consuming disproportionate space, and suggest data structure optimizations. If your sorted sets are growing unbounded because old entries are not being trimmed, the assistant can spot that by inspecting the key sizes directly.

Configuration

{
  "mcpServers": {
    "redis": {
      "command": "npx",
      "args": ["-y", "redis-mcp"],
      "env": {
        "REDIS_URL": "redis://localhost:6379"
      }
    }
  }
}

Replace the URL with your Redis connection string. As with the database server, use a development instance rather than production.

The Backend Developer Stack -- Combining Everything

These five servers cover the core systems that backend developers interact with daily. Together, they create a workflow where your assistant understands the full vertical slice of your application:

1. Data Layer: PostgreSQL MCP reads schemas, runs queries, and generates migrations. Redis MCP inspects the cache and verifies data consistency.
2. Infrastructure: Docker MCP manages your local development environment and troubleshoots container issues.
3. Collaboration: GitHub MCP handles PRs, code reviews, and issue management.
4. Reliability: Sentry MCP monitors errors and performance, feeding production insights directly into your development workflow.

Here is what an integrated workflow looks like. A Sentry alert fires for a new error in production. Your assistant queries Sentry MCP to read the stack trace and affected endpoint. It checks the relevant code, queries the Postgres schema to understand the data model, and identifies a query that fails when a nullable column contains null. It writes the fix, checks that the Docker-based test environment is running, verifies the fix against the test database, creates a GitHub PR with a description that references the Sentry issue, and marks the error as resolved. Five systems, one conversation, zero browser tabs.

Getting Started

Prioritize based on where you lose the most time:

• Writing and debugging SQL constantly? PostgreSQL MCP pays for itself immediately.
• Fighting Docker issues regularly? Docker MCP eliminates the container debugging dance.
• Doing a lot of code review? GitHub MCP streamlines the entire PR workflow.
• On-call or dealing with production errors? Sentry MCP brings monitoring data into your editor.
• Debugging cache-related bugs? Redis MCP gives you visibility into your cache state.

The total token overhead for the full stack is around 29,800 tokens. PostgreSQL, Sentry, GitHub, and Redis each contribute roughly 4,000-10,000 tokens. Docker adds about 7,200. If that feels heavy, start with just Postgres and Sentry -- those two alone eliminate the most frequent context switches for most backend developers.

For a pre-configured setup with all five servers, check out the Backend Developer Stack on stackmcp.dev. It includes ready-to-paste configurations for Claude Code, Cursor, Windsurf, and other supported clients.