AI coding agent context for large codebases

Model selection matters less than the harness around the model. ProtoMap is a context engine and model harness for gRPC and Protobuf: it gives Claude Code and Codex organization-wide context, refreshed at development speed, with no full local checkout.

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MessageDispatchCoordination has 42 callers and 6 callees, five hops out each way:

Every caller goes through its one method, Dispatch. AuthService is among its 6 callees. Invoicing services, such as CreditNotePublication, call it most.

Multi-hop service dependencies

Which services depend on MessageDispatchCoordination?

Every service that depends on it, directly or through other services, grouped by domain, with the direct callers and their call sites.

Direct callers

Invoicing (18): CreditNotePublication and 17 more. Approvals (9): ApprovalThresholdAssessment and 8 more. Direct callers (5) call Dispatch from 7 call sites.

The harness problem

Model quality is necessary. It is not sufficient.

The same model that ships a clean PR on a 10k-line project struggles on a monorepo. The bottleneck moves from the model to the harness around it: context, permissions, tools, and freshness. That harness decides whether a competent model succeeds on a real codebase or wastes tokens on grep, and it is what an engineering org actually has to build.

What CLAUDE.md and AGENTS.md solve

Conventions, repo orientation, “where the tests live,” coding standards. These are prose guidance an agent reads once. They scale to setup, not to “which 14 services call this RPC.”

Where they fall short

Cross-repo consumer mapping. Field-level usage. Change-impact analysis. The questions an agent has to answer to safely modify a shared API in a system it didn’t write.

The questions behind every cross-service change, answered from code.

1. Dependents
2. Method callers
3. Field changes
4. Unused RPCs

Method dependencies

Who calls this RPC, and from where?

Callers and callees of a method two hops out, plus every call site with the calling method and its file location.

Unused RPCs

Which RPCs can we clean up?

RPCs with no callers or call sites in the scanned code, plus what to check before you remove each one.

Works with every major AI coding agent

Any agent that implements the MCP protocol.

Once an engineer is ready to apply a change, the coding agent pulls the dependencies directly from ProtoMap over MCP.

Frequently asked questions

How does this differ from AGENTS.md and CLAUDE.md?

AGENTS.md and CLAUDE.md hold conventions, repo orientation, and prose guidance an agent should read once. ProtoMap provides structured, queryable context about every API and consumer in your code, refreshed on every commit. The two are complementary: the markdown files tell the agent how to behave, ProtoMap tells it what is in the codebase.

Does this replace retrieval-augmented generation or embeddings?

No. RAG and semantic retrieval are good at finding text that resembles a query. ProtoMap is good at answering deterministic structural questions: which services call this RPC, which fields are consumed where, what breaks if I rename this. The two approaches complement each other.