Want your AI copilot to do more than autocomplete? MCP servers plug it into your real tools — files, Git, APIs and docs — so it can take useful actions for you.
For a practical roundup, see this overview from The New Stack: 10 MCP servers for frontend developers. Below is a quick, hands-on guide to what they unlock and how to start fast.
What is MCP (in 10 seconds)?
MCP is an open protocol that lets AI apps securely connect to tools as “servers” (file systems, Git, APIs, browsers). Your model can then read, plan and act with guardrails.
Read the spec and ecosystem on GitHub: Model Context Protocol.
10 MCP servers to try (and what they unlock)
- File System: Search, read and edit project files. Great for refactors and config tweaks.
- Git: Create branches, stage diffs and craft commit messages from generated changes.
- GitHub: Open issues/PRs, request reviews and reference CI status directly in chat.
- Browser: Load web pages, scrape docs and pull examples with citations.
- Web Search: Compare sources, summarize results and link back for verification.
- OpenAPI: Call your REST endpoints from a schema, useful for testing and mock data.
- Database/SQL: Inspect schemas, run safe queries and generate reports from app data.
- Package Registry (npm): Audit packages, suggest upgrades and explain breaking changes.
- Testing (Playwright/Cypress): Generate tests, run them and capture failure context.
- Project Management (Jira/GitHub Issues): File bugs, link PRs and update statuses.
Fast setup (3 steps)
- Install an MCP‑enabled client (e.g., Claude desktop or another compatible IDE plugin).
- Add a server from its README to your client config (scopes, paths, API keys).
- Test with a small task: “Open a new branch, update this config and draft a PR.”
Best practices and guardrails
- Use read‑only by default; enable write access per directory or repo.
- Prefer “dry runs” and require diffs before committing or deploying.
- Scope credentials tightly (least privilege, short‑lived tokens).
- Template prompts to require a plan, assumptions and a rollback step.
- Log actions to a reviewable audit trail; pair with CI checks.
Takeaway
MCP turns your AI copilot from a chatty assistant into a productive teammate. Start with file, Git and OpenAPI servers, then layer deployment and test tooling.
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