Show HN: Service-catalog-MCP – Index your codebase and make batch changes

Description and use

**Brief Summary (2‑3 sentences)**
Service‑catalog‑MCP is an open‑source tool that indexes a codebase and lets you apply batch changes through a standard Model Context Protocol (MCP) interface. By exposing your repository as a searchable, programmable catalog, it enables AI assistants and other automation agents to discover, query, and modify code in a controlled way. The project is currently at a medium‑readiness level—suitable for prototypes or internal tooling after a quick manual review.

**Value**

• **Standardised AI‑to‑tool communication** – MCP provides a uniform protocol for connecting large‑language‑model (LLM) agents to real code‑base operations, eliminating the need for custom adapters.
• **Batch refactoring & automation** – Once indexed, you can issue declarative change requests (e.g., rename a function across dozens of files) that the service executes safely, dramatically speeding up large‑scale maintenance tasks.
• **Reusable service catalog** – The indexed view can be reused by multiple agents or CI pipelines, turning a static repo into a queryable knowledge source.

**Practical Adoption Path**

| Step | Action | Why |
|------|--------|-----|
| 1️⃣ | **Run a quick feasibility test** – Clone the repo, spin up the MCP server locally, and point it at a small, non‑critical part of your codebase. | Confirms that the indexing pipeline works with your language stack and that the API surface matches your needs. |
| 2️⃣ | **Review metadata & licensing** – Check the LICENSE file, open‑issue backlog, and recent commit activity (last update 2026‑06‑23). | Mitigates the risk of hidden legal or maintenance problems. |
| 3️⃣ | **Add integration tests** – Write a few end‑to‑end tests that issue a “search‑and‑replace” batch request and verify the resulting diff. | Guarantees that the service behaves as expected before you expose it to production. |
| 4️⃣ | **Wrap in an internal API gateway** – Deploy the MCP server behind your internal authentication layer and expose only the needed RPC methods. | Provides security, auditability, and version control for AI‑driven changes. |
| 5️⃣ | **Pilot with a single AI agent** – Connect one internal LLM‑assistant (e.g., a code‑review bot) to the MCP endpoint and monitor the change logs. | Allows you to assess real‑world impact while keeping the blast radius limited. |
| 6️⃣ | **Scale & harden** – Add RBAC, rate‑limiting, and CI/CD pipelines that automatically re‑index after each merge. | Turns the prototype into a production‑ready service. |

**Production Readiness**

• **Maturity**: Medium. The project is functional and recently updated, but integration signals are sparse, and documentation is minimal.
• **Risks**: Limited quality signals, unknown long‑term maintenance cadence, and a need for manual vetting of licensing and security posture.
• **Recommended Use**: Ideal for internal prototypes, developer tooling, or controlled batch‑refactoring jobs. Before production, perform the adoption steps above, add comprehensive tests, and establish a maintenance contract (e.g., fork and keep a CI watchdog).

In short, Service‑catalog‑MCP can become a powerful backbone for AI‑driven code operations, provided you invest the modest effort required to validate, test, and harden the integration.

Use in a workflow

Show HN: Service-catalog-MCP – Index your codebase and make batch changes helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.