Every tool, one connection - for the whole team.

Developer-tools MCPs connect AI agents to the APIs, services, and infrastructure that engineering teams already use. A team reaches for these when they want an LLM to query logs, trigger deployments, search documentation, or interact with third-party platforms without writing custom integration code. The distinguishing factor is tool breadth and authentication flexibility — the best options expose multiple related endpoints and handle OAuth, API keys, and webhook signatures cleanly. Choosing comes down to two axes: specificity versus generality, and read versus write access. Narrow MCPs (single-service wrappers) are faster to audit and safer to grant broad permissions. General-purpose MCPs (multi-service hubs) reduce the number of connections you manage but require tighter scoping. Decide whether your agent needs read-only data retrieval or the ability to mutate state, then match that to the MCP's permission model.