LedgerLens MCP
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LedgerLens MCP
A production-grade Model Context Protocol server for a fictional Indian neobank — built the way a regulated fintech would build it.
Documentation · Architecture · Decisions · Glossary
AI agents connect to LedgerLens to investigate customers, payments, KYC and AML alerts at Kosha Financial, take audited, idempotent write actions, and search 85 internal SOPs and policies with cited, permission-aware hybrid retrieval. Every piece of data is synthetic but regulator-realistic: UPI, IMPS and NEFT rails, PAN and Aadhaar handling under the RBI KYC Master Direction, PMLA-style alert typologies.
Highlights
OAuth 2.1 to the MCP spec | Protected Resource Metadata (RFC 9728) discovery, Cognito, per-tool scopes, role-based PII masking, append-only audit log |
Resilience you can watch | Deadline propagation, retries with jitter and budgets, circuit breakers, bulkheads, per-client rate limits, idempotency, chaos injection |
Hybrid RAG with citations | pgvector + Postgres full-text fused by reciprocal rank fusion, row-level security on document classification, graceful degradation |
Serverless on AWS | Lambda container images, CDK, keyless OIDC deploys, CodeDeploy canaries with automatic rollback |
Supply-chain hygiene | SHA-pinned actions, digest-pinned images, Trivy and pip-audit gates, gitleaks, Dependabot with cooldown, protected |
Related MCP server: @deepidv/mcp-server
Build progress
Delivered in phases; each phase lands as one reviewed pull request with green CI. Full plan: docs/plans/v1.0-architecture.md.
Phase | Scope | Status |
P0 | Repository, uv workspace, CI/CD, hardened Docker image, design docs, ADRs | ✅ v0.2.0 |
P1 | Deterministic synthetic data generator, PostgreSQL schema and migrations | 🚧 P1a domain package done · P1b schema next |
P2 | Core Banking API: customers, accounts, ledger, KYC, AML, disputes | |
P3 | MCP gateway: tools, resources, prompts, OAuth, masking, audit | |
P4 | Resilience toolkit, chaos mode, screening vendor simulator | |
P5 | SOP corpus, SQS ingestion pipeline, hybrid retrieval, RAG evals | |
P6 | AWS CDK, OIDC deploys, CodeDeploy canaries, end-to-end and load tests | |
P7 | LangGraph and Google ADK agents, Streamlit console, demo | |
P8 | Showcase polish: animated infographics, docs site, walkthrough |
Quick start
Requires uv and Docker.
git clone https://github.com/sourav-dhar/ledgerlens-mcp && cd ledgerlens-mcp
uv sync --all-packages # Python 3.13 and every workspace package
uv run poe check # lint, strict type check, tests
uv run poe up # gateway :8700 · core API :8701 · screening sim :8702Call a tool over MCP Streamable HTTP:
curl -s localhost:8700/mcp \
-H 'Content-Type: application/json' -H 'Accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_service_health","arguments":{}}}'Repository layout
services/ core-api (FastAPI) · mcp-gateway (FastMCP) · screening-sim (FastAPI)
docker/ one multi-stage image recipe for every service (Lambda Web Adapter)
docs/ plans · architecture (HLD + 9 LLDs) · ADRs · diagrams · MkDocs site
.github/ CI, docs, release workflows · Dependabot · templatesEngineering standards
Test-first, with a failure matrix per tool (auth, scopes, masking, timeouts, breaker open, rate limits, idempotency).
Strict gates: ruff, pyright strict at zero errors, pytest with warnings as errors, ≥ 90% coverage per package.
Reproducible images: code-only rebuilds in ~7 s via a bit-for-bit reproducible dependency layer; non-root, read-only, all capabilities dropped.
Decisions are written down: 10 ADRs, a domain glossary and versioned plans.
Kosha Financial, its staff and its customers are fictional; all data is synthetic. Real regulations are cited for realism only — nothing here is legal or compliance advice. MIT licensed.
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Maintenance
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