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PQC Khepra MCP Server: Agentic Security Attestation Framework

KHEPRA MCP Server smithery badge MCP Registry License Container PQC Sovereign compliance engine with 36,195 STIG/CCI/NIST/CMMC mappings. Air-gappable. Zero token costs. Run ert_scan → get a Godfather Report with dollar-denominated business impact. The only MCP compliance server that runs on your metal — with the World's First DoD PQC STIG built in. PQC-01-STIG-V1R1 — Full Whitepaper → 17 controls covering CNSA 2.0, FIPS 203/204/205, and the NSA's May 2026 MCP security advisory. The world's first DoD-style Post-Quantum Cryptography STIG, including the first PQC controls for agentic AI and MCP deployments. Tiers Tier License Key Tools Telemetry Egress Community ❌ Not required pqc_stig + 12 core tools Opt-in Dark Crypto Intel Zero (sovereign mode) Sovereign ✅ Required All 34 tools Zero Zero Pharaoh ✅ Required All 34 tools + priority support Zero Zero Community tier is free. Run pqc_stig to assess your project's quantum readiness against PQC-01-STIG-V1R1 — the World's First DoD-style Post-Quantum Cryptography STIG — no license key needed. What It Does KHEPRA MCP connects your AI assistant directly to a hardened compliance engine. Ask Claude or any MCP client to scan a system, map findings to STIG/NIST/CMMC controls, and generate an executive-ready risk report — all without sending data to external APIs. Key capabilities: 36,195 STIG/CCI/NIST 800-53/800-171/CMMC mappings (offline, bundled) Post-quantum cryptographic attestation on every tool call (ML-DSA-65 / FIPS 204) World's First DoD PQC STIG — 17 controls covering CNSA 2.0 / FIPS 203/204/205 + agentic AI / MCP (PQC-01-STIG-V1R1) Godfather Report: dollar-denominated business impact per finding (FAIR model) Air-gap and SCIF compatible — sovereign/ironbank modes make zero egress calls Flat annual licensing — no per-token or per-query charges Runs on your metal: on-prem, DoD, IC, classified environments Installation There are two delivery methods: Docker (recommended, no build required) and compiled binary (fastest startup, required for air-gap). Both support the same environment variables and all MCP clients. Choose your path: Method Best For Startup Docker Most users, easiest setup ~2s Compiled Binary Air-gap, SCIF, performance ~300ms Option A: Docker (Recommended) Requires Docker Desktop or Docker Engine. The image is pre-built and ships the full compliance database — no additional downloads in sovereign mode. # Pull once docker pull ghcr.io/nouchix/pqc-khepra-mcp:latest # Test it (should print the initialize response and exit) echo '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}},"id":0}' \ | docker run --rm -i -e KHEPRA_MODE=sovereign ghcr.io/nouchix/pqc-khepra-mcp:latest Option B: Compiled Binary Requires Go 1.21+ for building, or download a pre-built release from GitHub Releases. git clone https://github.com/nouchix/PQC-Khepra-MCP.git cd PQC-Khepra-MCP # Build (cross-compile for your OS) go build -o khepra-mcp ./cmd/khepra-mcp # Linux / macOS go build -o khepra-mcp.exe ./cmd/khepra-mcp # Windows # Test the binary echo '{"jsonrpc":"2.0","method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}},"id":0}' \ | KHEPRA_MODE=sovereign ./khepra-mcp Windows — using the batch launcher The repo ships a run-mcp.bat launcher for Windows. It uses the pre-built binary (fast path) and falls back to go run automatically: :: run-mcp.bat is already in the repo at the root of PQC-Khepra-MCP :: Point your MCP client to: cmd /c C:\path\to\PQC-Khepra-MCP\run-mcp.bat Adding to Your AI Client Claude Desktop Config file location: macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json Community tier — Docker (macOS / Linux) { "mcpServers": { "khepra": { "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_MODE=sovereign", "-v", "/var/lib/khepra:/var/lib/khepra", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ] } } } Community tier — Docker (Windows) { "mcpServers": { "khepra": { "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_MODE=sovereign", "-v", "C:\\Users\\YourName\\.khepra:/var/lib/khepra", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ] } } } Community tier — Binary (Windows, fastest startup) { "mcpServers": { "khepra": { "command": "C:\\path\\to\\PQC-Khepra-MCP\\khepra-mcp.exe", "args": [], "env": { "KHEPRA_MODE": "sovereign", "KHEPRA_NETWORK_POLICY": "lan", "MCP_PQC_ENABLED": "true", "KHEPRA_MANIFEST_PATH": "C:\\path\\to\\PQC-Khepra-MCP\\manifest.json" } } } } Community tier — Binary via batch launcher (Windows) { "mcpServers": { "khepra": { "command": "cmd", "args": ["/c", "C:\\path\\to\\PQC-Khepra-MCP\\run-mcp.bat"], "env": { "KHEPRA_MODE": "sovereign", "KHEPRA_NETWORK_POLICY": "lan", "MCP_PQC_ENABLED": "true" } } } } Sovereign / Pharaoh tier (with license key) { "mcpServers": { "khepra": { "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_LICENSE_KEY", "-e", "KHEPRA_MODE=sovereign", "-v", "/var/lib/khepra:/var/lib/khepra", "-v", "/var/log/khepra:/var/log/khepra", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ], "env": { "KHEPRA_LICENSE_KEY": "YOUR_LICENSE_KEY_HERE" } } } } After editing, restart Claude Desktop. Verify in Settings → Developer — you should see khepra with status running and all tools listed. Cursor Config file: .cursor/mcp.json in your project root, or ~/.cursor/mcp.json globally. Docker (macOS / Linux) { "servers": { "khepra": { "type": "stdio", "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_MODE=sovereign", "-v", "/var/lib/khepra:/var/lib/khepra", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ] } } } Binary (macOS / Linux) { "servers": { "khepra": { "type": "stdio", "command": "/path/to/khepra-mcp", "args": [], "env": { "KHEPRA_MODE": "sovereign", "KHEPRA_MANIFEST_PATH": "/path/to/PQC-Khepra-MCP/manifest.json" } } } } Binary (Windows) { "servers": { "khepra": { "type": "stdio", "command": "C:\\path\\to\\PQC-Khepra-MCP\\khepra-mcp.exe", "args": [], "env": { "KHEPRA_MODE": "sovereign", "KHEPRA_MANIFEST_PATH": "C:\\path\\to\\PQC-Khepra-MCP\\manifest.json" } } } } VS Code (with GitHub Copilot or Cline extension) Config file: .vscode/mcp.json in your project, or user settings. { "servers": { "khepra": { "type": "stdio", "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_MODE=sovereign", "-v", "${env:HOME}/.khepra:/var/lib/khepra", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ] } } } Or via user settings.json for the Cline extension: { "cline.mcpServers": { "khepra": { "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_MODE=sovereign", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ] } } } Windsurf Config file: ~/.codeium/windsurf/mcp_config.json { "mcpServers": { "khepra": { "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_MODE=sovereign", "-v", "/var/lib/khepra:/var/lib/khepra", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ] } } } Continue.dev Config file: ~/.continue/config.json — add to the experimental.modelContextProtocolServers array: { "experimental": { "modelContextProtocolServers": [ { "name": "khepra", "transport": { "type": "stdio", "command": "docker", "args": [ "run", "--rm", "-i", "-e", "KHEPRA_MODE=sovereign", "ghcr.io/nouchix/pqc-khepra-mcp:latest" ] } } ] } } Cloud / SaaS AI Tools (Claude.ai, ChatGPT, Gemini, etc.) Cloud-based AI tools cannot directly spawn local subprocesses — they need an HTTP/SSE bridge to reach your local KHEPRA server. There are two approaches: Approach 1 — mcp-remote proxy (easiest, no server required) mcp-remote tunnels a local stdio MCP server over HTTPS, making it accessible to any cloud tool. This is what the Kaggle MCP entry in the config above uses. # Install once npm install -g mcp-remote # Start the bridge (exposes your local KHEPRA server at https://localhost:3000) KHEPRA_MODE=sovereign mcp-remote \ --server "docker run --rm -i -e KHEPRA_MODE=sovereign ghcr.io/nouchix/pqc-khepra-mcp:latest" \ --port 3000 Then in Claude.ai (or any cloud tool that accepts MCP SSE URLs): MCP Server URL: http://localhost:3000/sse Security note: mcp-remote binds to localhost by default. Do not expose it to the public internet without TLS and authentication. In sovereign/ironbank mode, KHEPRA itself makes zero egress calls — only the bridge connection to the cloud tool carries data. Approach 2 — Self-hosted HTTP/SSE endpoint For teams running KHEPRA on a shared server (e.g., Hostinger VPS at IP_ADDRESS), start the server in HTTP mode: # On your server — start KHEPRA in HTTP/SSE mode docker run -d \ -e KHEPRA_MODE=hybrid \ -e KHEPRA_HTTP_PORT=8443 \ -e KHEPRA_LICENSE_KEY="${KHEPRA_LICENSE_KEY}" \ -p 8443:8443 \ ghcr.io/nouchix/pqc-khepra-mcp:latest # Point your cloud tool to: # https://your-server.com:8443/sse Then configure any cloud AI tool that supports MCP SSE: Cloud Tool Where to add MCP URL Claude.ai (Pro/Team) Settings → Integrations → MCP Servers OpenAI Assistants API tools field with type: "mcp" Gemini for Workspace Extensions → Custom MCP (preview) Glama.ai Workspace → MCP Servers Smithery.ai Catalog → Self-hosted server Note: HTTP/SSE mode (hybrid/edge) enables external connections. Always terminate TLS at a reverse proxy (nginx/Caddy) and restrict access by IP or API key. The sovereign mode refuses HTTP connections by design — air-gap integrity is preserved. Approach 3 — Smithery / MCP Registry (Community tier only) KHEPRA is listed on Smithery.ai and the MCP Registry. Cloud tools that support registry-based discovery can install it directly: Registry ID: io.github.nouchix/pqc-khepra-mcp This runs the Community tier via Smithery's managed infrastructure. For sovereign deployment (air-gap, your data stays on your metal), use Options A or B above. Validation — Test Your Installation Run this from your terminal to verify the server responds correctly: # Docker echo '{"jsonrpc":"2.0","method":"tools/list","params":{},"id":1}' \ | docker run --rm -i -e KHEPRA_MODE=sovereign ghcr.io/nouchix/pqc-khepra-mcp:latest # Binary (Linux / macOS) echo '{"jsonrpc":"2.0","method":"tools/list","params":{},"id":1}' \ | KHEPRA_MODE=sovereign ./khepra-mcp # Binary (Windows PowerShell) '{"jsonrpc":"2.0","method":"tools/list","params":{},"id":1}' \ | & ".\khepra-mcp.exe" Expected output: a JSON-RPC response listing all available tools. If you see "tools": [...] with 12+ entries — you're connected. Full protocol validation (Windows) # Runs the complete Claude Desktop handshake sequence and validates all responses .\scripts\test-mcp-handshake.ps1 -BinaryPath ".\khepra-mcp.exe" # Expected output: # [PASS] initialize | protocolVersion=2025-11-25 | listChanged=False # [PASS] tools/list | count=34 # TRL-10 READY - Server passes full Claude Desktop protocol validation MCP Tools Community Tier (Free — No License Key) pqc_stig — World's First DoD PQC STIG ⭐ Assesses a source code directory against PQC-01-STIG-V1R1: 12 controls covering CNSA 2.0 algorithm approval, ML-DSA-65 key strength, ML-KEM-768 encapsulation, hybrid cryptography, key storage, constant-time implementation, and certificate chain requirements. pqc_stig(scan_path?: string, profile?: "quick" | "full" | "executive") Example: "Run pqc_stig on my project and tell me if I'm CNSA 2.0 compliant" nist_map Map CCI identifiers or STIG findings to NIST 800-53 Rev 5 controls. khepra_query_stig Query the 36,195-row STIG/CCI/NIST/CMMC compliance database by control ID. dark_crypto_contribute (opt-in) Contribute anonymized cryptographic algorithm telemetry to the SouHimBou AI Dark Crypto Intelligence Network. No PII. Opt-in only — never fires without explicit invocation. Sovereign / Pharaoh Tier ert_scan Enterprise Risk & Threat scan across STIG, NIST 800-53, NIST 800-171, CMMC, and FedRAMP. Returns Godfather Report with dollar-denominated business impact. ert_scan(target: string, frameworks?: string[], output_format?: "godfather" | "json" | "csv") Example: "Run ert_scan on /etc and generate a Godfather Report" stig_check Automated RHEL-09-STIG-V1R3 compliance scan against a live system or configuration path. cmmc_assess Full CMMC Level 1, 2, or 3 assessment with gap analysis and POA&M generation. godfather_report Generate an executive Godfather Report from prior scan results: top 10 findings ranked by dollar exposure, remediation ROI, and FAIR model business impact. + 20 additional tools agent_record, dag_attestation, flight_export, khepra_get_dag_chain, nhi_inventory, acp_status, owasp_agent_assess, khepra_export_attestation, khepra_export_poam, khepra_get_compliance_score, ert_crypto, ert_readiness, stig_benchmark, ir_analysis, vuln_hunter, sbom_generate, threat_model, khepra_query_threat_intel, discover_assets, and more. The Godfather Report Unlike compliance scanners that output a wall of CVEs, KHEPRA translates findings into the language executives care about: Finding: RHEL-09-212030 — No FIPS-validated crypto on /etc/ssh Severity: CAT I (HIGH) Business Impact: $2.4M estimated breach exposure (FAIR model) Remediation Cost: $800 (4 hours engineer time) ROI: 3,000x Every finding includes control ID, framework mapping, business impact in dollars, remediation cost estimate, and ROI. Deployment Modes Mode Air-Gap Egress Telemetry Use Case sovereign ✅ Yes Zero Zero On-prem, SCIF, classified (DEFAULT) ironbank ✅ Yes Zero Zero DoD/IC production, FIPS-only hybrid ❌ No LAN Zero Edge + cloud coordination edge ❌ No Unrestricted Zero Fully stateless SaaS Set via KHEPRA_MODE environment variable. Unknown values are rejected at startup and fall back to sovereign (fail-closed). Environment Variables Variable Required Default Description KHEPRA_LICENSE_KEY Sovereign/Pharaoh only — License key. Community tier runs without one. Get at nouchix.com KHEPRA_MODE No sovereign Deployment mode: sovereign, ironbank, hybrid, edge KHEPRA_MANIFEST_PATH No manifest.json Path to signed tool manifest file KHEPRA_HOME No /var/lib/khepra Data and compliance DB directory KHEPRA_LOG_DIR No /var/log/khepra Log directory KHEPRA_DAG_PATH No ~/.khepra/dag DAG audit chain storage path KHEPRA_AUDIT_LOG_PATH No ~/.khepra/audit.ndjson Signed audit log path KHEPRA_MAX_CONCURRENT No 5 Max concurrent tool calls per agent KHEPRA_NETWORK_POLICY No lan Network scope: lan, none, unrestricted MCP_PQC_ENABLED No true Enable ML-DSA-65 PQC attestation on all responses Air-Gap & SCIF Deployment KHEPRA makes zero external network calls in sovereign and ironbank modes: License validated offline via ML-DSA-65 signed license.adinkhepra file Compliance databases (36,195 mappings) bundled in container — no external downloads No telemetry, no heartbeat, no egress — verified at the transport layer # Transfer image to air-gapped network docker save ghcr.io/nouchix/pqc-khepra-mcp:latest | gzip > khepra-mcp.tar.gz # On air-gapped host: docker load < khepra-mcp.tar.gz Note on telemetry: The dark_crypto_contribute tool (Community tier) sends anonymized cryptographic algorithm telemetry to the SouHimBou AI intelligence network only when explicitly invoked by the user. It is never triggered automatically. In sovereign/ironbank mode, all network calls are blocked at the transport layer regardless. Compliance Coverage Framework Version Mappings STIG (RHEL 9) V1R3 Automated scanning NIST 800-53 Rev 5 2,120 CCIs NIST 800-171 Rev 2 320 controls CMMC Level 3 Full practice set FedRAMP High Baseline scanning PQC-01-STIG-V1R1 V1R1 17 PQC controls (CNSA 2.0) Total 36,195+ mappings Licensing Flat annual licensing — no per-token or per-query charges. Tier Cost License Key Tools Community Free Not required pqc_stig + 12 core tools Sovereign Annual flat fee Required All 34 tools, air-gap, on-prem Pharaoh Annual flat fee Required All 34 tools + priority support + SLA Community tier is permanently free — contribute to open-source PQC adoption Sovereign/Pharaoh: contact contact@nouchix.com or visit nouchix.com Security Reporting Vulnerabilities Do not open public issues for security vulnerabilities. Report privately via GitHub Security Advisories or email support@nouchix.com. SLA Target Acknowledgement 24 hours Initial assessment 5 business days Patch / mitigation (Critical) 30 days We accept encrypted reports via PGP (keys/security_contact.asc) and Post-Quantum channels (Dilithium / ML-DSA-65 keys in keys/). See SECURITY.md for the full disclosure policy and ASAF event taxonomy. Security Posture Deploying advanced post-quantum cryptography, air-gapped isolation, and comprehensive STIG mappings — built in direct alignment with NSA & ASD Model Context Protocol guidelines. NSA & ASD MCP Security Alignment The NSA and Australian Signals Directorate (ASD) have published specific threat vectors for AI systems interacting with local environments. KHEPRA MCP is explicitly designed to mitigate every identified vector: NSA/ASD Requirement KHEPRA Implementation Cryptographic validation of tool responses ML-DSA-65 (Dilithium) signatures on all JSON-RPC 2.0 payloads Input validation & sanitization Parameter injection resistance via strict JSON Schema validation Principle of least privilege credentials Short-lived ephemeral tokens tied to specific task execution windows Comprehensive audit logging Tamper-evident events compiled into an immutable DAG structure Resource consumption limits Rate limiting + backpressure for LLM request loops Authorization gates for sensitive actions Human-in-the-loop gate for destructive state changes Environment isolation Containerized execution with zero-egress sovereign mode Software supply chain integrity Manifest pinning for all loaded tools and dependencies Network exposure reduction Air-gappable — zero internet transit in sovereign/ironbank modes Post-quantum resilience PQC-signed DAG trail protecting against harvest-now-decrypt-later Compliance Certifications Framework Status Coverage CMMC Level 2 ✅ Automates evidence collection for AU, CM, SI, SC domains NIST SP 800-171 Rev 2 ✅ Logging, accountability, system integrity NIST SP 800-53 Rev 5 ✅ Continuous monitoring (AU-2, SI-4) FIPS 203 (ML-KEM) ✅ Key encapsulation for secure transit FIPS 204 (ML-DSA) ✅ Digital signatures for payload authentication NSM-10 PQC Mandate ✅ National Security Memorandum 10 compliance DFARS 252.204-7012 ✅ Immutable forensic trails for cyber incident reporting NSA MCP Security Guidelines ✅ Direct mapping to all published AI agent threat mitigations Live Deployment — Physical Edge Running continuously on constrained edge hardware since May 12, 2026 to prove efficiency in sovereign environments: Hardware: Raspberry Pi 2 · 1 GB RAM · 900 MHz ARM · Live Spectrum Router SCADA Pod: STM32U585 / QRB2210 · Modbus TCP · MQTT · Zephyr RTOS 3.4+ · Live Dilithium Signature Verification Controls active: 3 open ports secured · 12 STIG violations detected · 100% file integrity monitoring (AIDE) · 24/7 continuous operation Academic Validation Event Date Institution UAlbany AI Plus Symposium 2026 — "KHEPRA Protocol: Quantum-Resilient Agentic

GovQL

# govql-mcp-server An MCP (Model Context Protocol) server for [GovQL](https://govql.us) — gives AI clients like Claude Desktop, Claude Code, and Cursor direct access to the US Congressional GraphQL API at [api.govql.us/graphql](https://api.govql.us/graphql) without bespoke HTTP wiring. For the design rationale (why FastMCP-Python, the passthrough+curated philosophy, roadmap through v0.4), see [design.md](https://github.com/govql/govql/blob/main/mcp-server/docs/design.md). ## What you can do with it Ask an agent questions like: - *"How did Vermont's two senators vote on the most recent nomination?"* - *"Which legislators in the 118th Congress switched parties during their service?"* - *"Compare Senator Sanders' voting record to Senator Murkowski's on cloture votes in the most recent Congress."* The agent picks the right tool, writes the GraphQL query against the live schema, and parses the response — no manual API wrangling. ## Install The server runs as a per-client subprocess over stdio. Pick your client: ### Claude Desktop Edit `claude_desktop_config.json` (Settings → Developer → Edit Config): ```json { "mcpServers": { "govql": { "command": "uvx", "args": ["govql-mcp-server"] } } } ``` Restart Claude Desktop. The `govql` tools appear in the tools panel. ### Claude Code Add to `.mcp.json` in your project (or `~/.mcp.json` for global): ```json { "mcpServers": { "govql": { "command": "uvx", "args": ["govql-mcp-server"] } } } ``` ### Cursor Settings → MCP → Add Server. Use the same `command` / `args` as above. ### Other clients Any MCP-compatible client that supports stdio servers will work. The command is `uvx govql-mcp-server` with no required arguments. ## Tools | Tool | Purpose | |---|---| | `execute_graphql` | Run any GraphQL query against the GovQL endpoint. Returns the result plus an `last_ingest` timestamp so the agent can reason about data freshness. | | `list_types` | Returns the names and kinds of every type in the GovQL schema. Optional `kind` filter (`"OBJECT"`, `"INPUT_OBJECT"`, `"ENUM"`, etc.) to narrow further. Start here when you don't know what's queryable. | | `describe_type` | Returns one type's full details — fields, arg signatures, input fields, enum values. Call after `list_types` to learn the shape of a specific type before writing a query. | ## Configuration All env vars are optional — the package is zero-config for end users. | Env var | Default | Purpose | |---|---|---| | `GOVQL_ENDPOINT` | `https://api.govql.us/graphql` | Endpoint to query. Override to point at a local dev stack. | | `GOVQL_TIMEOUT_MS` | `30000` | Per-request HTTP timeout. | | `LOG_LEVEL` | `INFO` | Logging level. Logs go to stderr only (stdout is reserved for the MCP transport). | ## Limits (enforced by the upstream API) - Max query depth: 10 - Max query complexity: ~10 billion points (`first: N` multiplies child cost by N — keep page sizes reasonable on deeply nested queries) - Rate limit: 100 requests / 60 s per source IP A depth or complexity violation surfaces as a GraphQL `errors` entry in the tool response so the agent can adjust and retry. ## Data freshness Every `execute_graphql` response includes a `last_ingest` ISO timestamp. Vote data refreshes hourly; legislator data refreshes daily. ## Status Version 0.1.0 ships three foundational tools: a GraphQL passthrough (`execute_graphql`) and two narrow schema-discovery tools (`list_types`, `describe_type`). Curated higher-level tools (`find_legislator`, `get_voting_record`, `compare_voters`, etc.) are planned for subsequent releases — see [design.md](https://github.com/govql/govql/blob/main/mcp-server/docs/design.md) for the roadmap. ## Links - [GovQL project site](https://govql.us) - [GraphQL API](https://api.govql.us/graphql) - [Source / issues](https://github.com/govql/govql)

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

Scholar Sidekick Mcp

Scholar Sidekick MCP resolves any scholarly identifier (DOI, PMID, PMCID, ISBN, arXiv, ISSN, NASA ADS bibcodes, WHO IRIS URLs) into structured bibliographic metadata, formats citations in 10,000+ CSL styles (Vancouver, APA, AMA, IEEE, Chicago, Harvard, MLA, Nature, BMJ, Lancet, and many more), and exports references to BibTeX, RIS, CSL JSON, EndNote (XML/Refer), RefWorks, MEDLINE, Zotero RDF, and CSV. The format/export/resolve tools accept a single identifier or a comma/newline-separated batch, so an assistant can chain resolveIdentifier → formatCitation → exportCitation in one prompt for an end-to-end "raw IDs → exportable bibliography" workflow. Three single-citation checks complete the picture: checkRetraction surfaces retractions, corrections, and expressions of concern from Crossref / Retraction Watch; checkOpenAccess returns OA status and the best legal landing or PDF URL from Unpaywall; verifyCitation cross-checks the cited title against the resolved record at the cited identifier to detect the AI-driven fabrication pattern documented by Topaz et al. (Lancet 2026 — the dominant pattern is a real DOI paired with an invented title, which simple identifier resolution cannot catch). Each formatted response carries a provenance metadata block (formatter, styleUsed, requestId, warnings) so you can see exactly which engine produced each citation. Try asking your assistant: "Is this citation real, or did an AI make it up? 10.1056/NEJMoa2033700 — 'Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine'" "Has PMID 30049270 been retracted?" "Resolve arXiv:2301.08745 and tell me if there's a free open-access PDF" "Format 10.1056/NEJMoa2033700 in Vancouver style and export as BibTeX" Powered by the Scholar Sidekick API (free tier available on RapidAPI).

Codegraph Mcp

# Transform any MCP-compatible LLM into a codebase expert through semantic intelligence A blazingly fast graphRAG implementation. 100% Rust for indexing and querying large codebases with natural language. Supports multiple embedding providers: modes cpu (no graph just AST parsing), onnx (blazingly fast medium quality embeddings with Qdrant/all-MiniLM-L6-v2-onnx) and Ollama (time consuming SOTA embeddings with hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M). I would argue this is the fastest codebase indexer on the Github atm. Includes a Rust SDK made stdio MCP server so that your agents can query the indexed codegraph with natural language and get deep insights from your codebase before starting development or making changes. Currently supports typescript, javascript, rust, go, Python and C++ codebases. 📊 Performance Benchmarking (M4 Max 128GB) Production Codebase Results (1,505 files, 2.5M lines, Python, Javascript, Typescript and Go) 🎉 INDEXING COMPLETE! 📊 Performance Summary ┌───────────────. ─┐ │ 📄 Files: 1,505 indexed │ │ 📝 Lines: 2,477,824 processed │ │ 🔧 Functions: 30,669 extracted │ │ 🏗️ Classes: 880 extracted │ │ 💾 Embeddings: 538,972 generated │ └───────────────. ─┘ Embedding Provider Performance Comparison Provider Time Quality Use Case 🧠 Ollama nomic-embed-code ~15-18h SOTA retrieval accuracy Production, smaller codebases ⚡ ONNX all-MiniLM-L6-v2 32m 22s Good general embeddings Large codebases, lunch-break indexing 📚 LEANN ~4h The next best thing I could find in Github CodeGraph Advantages ✅ Incremental Updates: Only reprocess changed files (LEANN can't do this) ✅ Provider Choice: Speed vs. quality optimization based on needs ✅ Memory Optimization: Automatic optimisations based on your system ✅ Production Ready: Index 2.5M lines while having lunch Read the README.md carefully the installation is complex and requires you to download the embedding model in onnx format and Ollama and setting up multiple environment variables (I would recommend setting these in your bash configuration)

Codegraph Rust

🎯 Overview CodeGraph is a powerful CLI tool that combines MCP (Model Context Protocol) server management with sophisticated code analysis capabilities. It provides a unified interface for indexing projects, managing embeddings, and running MCP servers with multiple transport options. All you now need is an Agent(s) to create your very own deep code and project knowledge synthehizer system! Key Capabilities 🔍 Advanced Code Analysis: Parse and analyze code across multiple languages using Tree-sitter 🚄 Dual Transport Support: Run MCP servers with STDIO, HTTP, or both simultaneously 🎯 Vector Search: Semantic code search using FAISS-powered vector embeddings 📊 Graph-Based Architecture: Navigate code relationships with RocksDB-backed graph storage ⚡ High Performance: Optimized for large codebases with parallel processing and batched embeddings 🔧 Flexible Configuration: Extensive configuration options for embedding models and performance tuning RAW PERFORMANCE ✨✨✨ 170K lines of rust code in 0.49sec! 21024 embeddings in 3:24mins! On M3 Pro 32GB Qdrant/all-MiniLM-L6-v2-onnx on CPU no Metal acceleration used! Parsing completed: 353/353 files, 169397 lines in 0.49s (714.5 files/s, 342852 lines/s) [00:03:24] [########################################] 21024/21024 Embeddings complete ✨ Features Core Features Project Indexing Multi-language support (Rust, Python, JavaScript, TypeScript, Go, Java, C++) Incremental indexing with file watching Parallel processing with configurable workers Smart caching for improved performance MCP Server Management STDIO transport for direct communication HTTP streaming with SSE support Dual transport mode for maximum flexibility Background daemon mode with PID management Code Search Semantic search using embeddings Exact match and fuzzy search Regex and AST-based queries Configurable similarity thresholds Architecture Analysis Component relationship mapping Dependency analysis Code pattern detection Architecture visualization support