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Memtrace

Memtrace — Structural Memory for AI Coding Agents The Problem Every AI coding agent — Claude Code, Cursor, Codex, Copilot — starts each turn completely blank. It re-reads raw source files and re-derives the full call graph, type hierarchy, and import tree from scratch on every single invocation. That structural rework burns 60–90% of the context window before any real reasoning begins. Less than 5% of tokens in a typical agentic coding session contribute genuine new intelligence. The rest is expensive, redundant noise — and it compounds: accuracy drops 40% as sessions grow, stale context crowds out signal, and summaries strip out the structural relationships agents need most. The Solution Memtrace is a bi-temporal structural memory layer that turns your codebase into a live, queryable knowledge graph — compiled from the AST, not guessed from embeddings. Every function, class, interface, and API endpoint becomes a typed node with deterministic relationships. Every file save becomes a queryable episode with timestamps, so agents can reason about structure, detect regressions, and time-travel through their own work without re-reading anything. One Rust binary. Zero configuration. Five-minute install. What agents can do with it Find callers, callees, and dependencies instantly — no file scanning, no token waste Compute blast radius before making a change — know exactly what breaks before anything is touched Detect structural drift between sessions — catch regressions the moment they happen, not at PR review Time-travel through code evolution — query any prior state of any symbol, not just git commits Search across the full codebase with hybrid retrieval — BM25 full-text + HNSW vector + graph traversal fused in one query Map API topology across services — cross-repo HTTP call graphs, dependency chains, dead endpoint detection Benefits −90% token cost on structural queries (Mem0) +26% accuracy on multi-step agentic tasks (Mem0) −91% p95 latency on structural lookups vs. RAG baselines +32.8% SWE-bench bug-fix success rate when agents have graph context (RepoGraph) 200–800ms per-save re-indexing — every file save is a queryable episode in under a second 40+ MCP tools covering indexing, search, relationships, impact analysis, temporal evolution, API topology, graph algorithms, and direct Cypher queries 12 languages + 3 IaC formats supported via Tree-sitter grammars Local-first, closed-source Rust — code never leaves the machine, no account required, no telemetry

Chain.Love MCP

## Overview ### what is Chain.Love MCP? Chain.Love MCP is a hosted remote MCP server and gateway for AI agents. It provides a single endpoint for discovering and comparing Web3 infrastructure services across 50+ blockchain networks, including RPCs, indexing, oracles, storage, compute, and developer tools. ### how to use Chain.Love MCP? To use Chain.Love MCP, add the hosted endpoint to your MCP client and connect to `https://app.chain.love/mcp` over Streamable HTTP. For public use cases, the basic MCP server URL is enough. For private downstream MCPs, add credentials only when required using `x-chainlove-cred-<credentialKey>` headers. ### key features of Chain.Love MCP? - Hosted remote MCP gateway for AI agents - Single endpoint for Web3 infrastructure discovery across 50+ blockchain networks - Aggregates infrastructure options across RPCs, indexing, oracles, storage, compute, and developer tools - Streamable HTTP transport - Public documentation and onboarding resources available online ### use cases of Chain.Love MCP? - Discovering and comparing Web3 infrastructure providers across many blockchain networks - Finding RPC, indexing, oracle, storage, compute, and developer tooling options through one MCP server - Giving AI agents a single hosted integration surface for Web3 infrastructure discovery - Reducing the need to integrate many separate provider-specific endpoints ### FAQ from Chain.Love MCP? - Can Chain.Love MCP be used as a hosted remote MCP server? Yes. Chain.Love MCP is designed to be consumed as a hosted remote MCP endpoint at `https://app.chain.love/mcp`. - Does Chain.Love MCP require credentials? Not always. Some downstream integrations may require credentials, which can be passed using `x-chainlove-cred-<credentialKey>` headers when needed. - How do I know which credential header to use? You can check the open-source Chain.Love registry at `https://github.com/Chain-Love/chain-love/blob/main/references/offers/mcpservers.csv` or browse `https://app.chain.love/toolbox/mcpservers` and look for the relevant `credentialKey` value. - Where can I learn more? Landing page: `https://www.chain.love/mcp-gateway` Documentation: `https://chain-love.gitbook.io/mcp-module`

Petro Mcp

petro-mcp — Petroleum Engineering MCP Server petro-mcp exposes petroleum engineering workflows to Claude and other MCP-compatible LLMs through natural language. Instead of writing Python scripts, just ask your AI assistant. Capabilities (80+ tools across the full upstream workflow): - Well Logs (LAS): Parse LAS files, extract curves and headers, compute Vshale, porosity (density, neutron-density, sonic, effective), water saturation (Archie, Simandoux, Indonesian), permeability (Timur, Coates), and net pay. - Decline Curve Analysis: Arps exponential/hyperbolic/harmonic fits, advanced models (Duong, PLE, SEPD), EUR calculation, Monte Carlo EUR distributions, bootstrap confidence intervals, probabilistic forecasts, price sensitivities. - Rate Transient Analysis (RTA): Agarwal-Gardner, Blasingame, NPI, flowing material balance, normalized rate, sqrt-time, material balance time, permeability estimation, radius of investigation. - Production Analytics: CSV production data queries, trend analysis, anomaly detection (shut-ins, rate jumps, water breakthrough, GOR blowouts), producing ratios (GOR, WOR, water cut). - PVT & Reservoir: Black-oil correlations (Standing, Beggs-Robinson, Hall-Yarborough, Lee-Gonzalez-Eakin, Sutton), brine PVT, bubble point, oil compressibility, gas Z-factor, volumetric OOIP/OGIP, recovery factors, Havlena-Odeh, P/Z analysis. - Drilling & Wellbore: Hydrostatic pressure, ECD, kill mud weight, MAASP, burst/collapse pressure, bit pressure drop, nozzle TFA, annular velocity, dogleg severity, vertical section, well survey, anticollision, wellbore tortuosity. - Production Engineering: Nodal analysis (Vogel IPR + VLP), Beggs-Brill multiphase flow, choke flow, erosional velocity, Turner/Coleman critical rates, hydrate temperature/inhibitor, ICP/FCP, HPT. - Economics: NPV, IRR, payout period, PV10, breakeven price, well economics, operating netback, price sensitivity. - Units: Oilfield unit conversions across pressure, rate, volume, length, density, viscosity, and more. Why petro-mcp? Purpose-built for petroleum engineers. Other energy MCP servers focus on commodity prices; this one runs the actual engineering calculations — log interpretation, decline analysis, reservoir engineering, drilling, production, and economics — all through plain English. Install: pip install petro-mcp → configure in Claude Desktop → ask away. Links: GitHub: https://github.com/petropt/petro-mcp · PyPI: https://pypi.org/project/petro-mcp/ · Web tools: https://tools.petropt.com License: MIT · Author: Groundwork Analytics