Architecting Developer Tools That Ship
Four production-ready MCP servers — including a full-stack Bayesian probabilistic inference engine (TypeScript + Julia/Turing.jl, NUTS sampler) for GHG emissions uncertainty quantification — enabling multi-domain queries across sensitive datasets without cloud exposure.
Open Source Projects
nzdpu-bayesian-emissions
github.com/dp-veritas/nzdpu-bayesian-emissions
Reported corporate emissions are point estimates masking true uncertainty of ±10% to ±100%. This dual-layer system pairs a TypeScript MCP server (SQLite, <100ms queries) with a Julia probabilistic engine (Turing.jl, NUTS sampler) to run MCMC inference over 33,630 records across 12,497 companies — returning p5–p95 posterior distributions rather than false precision. Cross-sector validated; runs fully air-gapped with Ollama or connects to Claude/Cursor for agentic workflows.
Key Capabilities
- — MCMC Actual Emissions Estimation — NUTS sampler returns p5–p95 posterior distributions, adjusting for boundary type (±0–40%), methodology quality (±0–30%), scope completeness (±0–60%), and temporal lag (±0–10%)
- — 6-Component Readiness Scoring — Evaluates Disclosure Depth, Methodology Quality, Scope Coverage, Peer Alignment, Temporal Consistency, and Verification Level before triggering the NUTS sampler (threshold: ≥60/100)
- — NGFS Scenario Projections — Monte Carlo simulation across Net Zero 2050, Delayed Transition, and Current Policies pathways with carbon price ranges and implied transition costs
- — Hierarchical Missing Data Imputation — Sector-calibrated priors estimate unreported Scope 3 categories (up to 15) using patterns across all 12,497 companies
- — Probabilistic Peer Benchmarking — Uncertainty-aware percentile placement with peer readiness comparison, not point-to-point ranking
- — Privacy-First Architecture — 100% offline after install; air-gapped capable with Ollama or connected to Claude/Cursor for multi-agentic workflows
Tools
mongodb-mcp-server
github.com/dp-veritas/mongodb-mcp-server
Secure MCP server enabling LLMs to query and analyze MongoDB databases. Safe for production use with comprehensive analytics and relationship discovery.
Key Capabilities
- — 24 Tools — Database discovery, document querying, aggregation pipelines, schema analysis
- — Relationship Discovery — Optimized relationship matrix (10-100x faster queries), graph traversal
- — AutoJoin Feature — Automatic $lookup generation for aggregation pipelines
- — Production-Safe — No write operations, schema modifications, or database changes
Tools
mcp-obsidian-tools
github.com/dp-veritas/mcp-obsidian-tools
MCP server exposing rich tools for querying Obsidian knowledge vaults. Read-only, path-validated security. Compatible with Cursor, VSCode, and Claude Desktop.
Tools
ollama-mcp-agent
github.com/dp-veritas/ollama-mcp-agent
Universal local AI agent connecting Ollama to any MCP server. Query vaults, emissions databases, MongoDB, or any data source through natural language. Supports 7B-30B+ parameter models locally, or 120B-671B+ cloud models. 100% offline capable, 100% sovereign.
Key Capabilities
- — Multi-Domain Queries — Connect multiple MCP servers simultaneously (Obsidian + NZDPU + MongoDB)
- — Intelligent Routing — Automatically selects the right data source based on query domain
- — Privacy-First — Air-gapped capable, no cloud dependencies, all data stays local
- — Model Flexibility — Supports 7B-30B+ parameter models locally, or optional 120B-671B+ cloud models
Use Cases
Join emissions data with research notes, query proprietary databases without cloud exposure, multi-domain analysis workflows