{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_f5g5p66ct599","handle":"black-litterman-mcp","url":"https://wellknown.network/agents/black-litterman-mcp","links":{"self":"https://wellknown.network/agents/black-litterman-mcp/record.json","html":"https://wellknown.network/agents/black-litterman-mcp","markdown":"https://wellknown.network/agents/black-litterman-mcp/record.md","api":"https://wellknown.network/api/v1/agents/black-litterman-mcp","status":"https://wellknown.network/api/v1/agents/black-litterman-mcp/status","claim":"https://wellknown.network/agents/black-litterman-mcp/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/black-litterman-mcp/claim.json","badge":"https://wellknown.network/agents/black-litterman-mcp/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/black-litterman-mcp/history","tools":"https://wellknown.network/api/v1/agents/black-litterman-mcp/tools"},"ard":{"identifier":"urn:air::server:black-litterman-mcp","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"black-litterman-mcp","summary":"Black-Litterman Portfolio Optimization MCP Server","description":"# Black-Litterman Portfolio Optimization MCP Server\n\n[![PyPI](https://img.shields.io/pypi/v/black-litterman-mcp)](https://pypi.org/project/black-litterman-mcp/)\n[![Python](https://img.shields.io/pypi/pyversions/black-litterman-mcp)](https://pypi.org/project/black-litterman-mcp/)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)\n\n**Black-Litterman portfolio optimization** MCP server for AI agents\n\nWorks with Claude Desktop, Windsurf IDE, Google ADK, and any MCP-compatible AI\n\n## Features\n\n- **Portfolio Optimization** - Calculate optimal weights using Black-Litterman model\n- **Investor Views** - Express views like \"AAPL will return 10%\" or \"NVDA will outperform MSFT\"\n- **Backtesting** - Validate strategies with historical data\n- **VaR Warnings** - Auto-warn on overly optimistic predictions using EGARCH model\n- **Multiple Assets** - S&P 500, NASDAQ 100, ETF, Crypto, and custom data support\n\n---\n\n## Quick Start (Claude Desktop)\n\n### Step 1: Find uvx path\n\nRun in terminal:\n```bash\nwhich uvx\n# Example output: /Users/USERNAME/.local/bin/uvx\n```\n\n> If uvx is not installed: `curl -LsSf https://astral.sh/uv/install.sh | sh`\n\n### Step 2: Configure Claude Desktop\n\nConfig file location:\n- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`\n- **Windows**: `%APPDATA%\\Claude\\claude_desktop_config.json`\n\nFile content (replace with your uvx path):\n\n```json\n{\n  \"mcpServers\": {\n    \"black-litterman\": {\n      \"command\": \"/Users/USERNAME/.local/bin/uvx\",\n      \"args\": [\"black-litterman-mcp\"]\n    }\n  }\n}\n```\n\n### Step 3: Restart Claude Desktop\n\n**Cmd+Q** (macOS) or fully quit and restart\n\n### Step 4: Use\n\nAsk Claude:\n\n> \"Optimize a portfolio with AAPL, MSFT, GOOGL. I think AAPL will return 10%.\"\n\n> **First run**: S&P 500 data auto-downloads (~30 seconds)\n\n> **Tip**: Want charts or dashboards? Just ask: \"Show me a dashboard with the results\" or \"Create a visualization of the portfolio weights\"\n\n### Example Use Cases\n\nTry these prompts with C…","publisher":null,"homepage":"https://github.com/irresi/bl-view-mcp#readme","repository":"https://github.com/irresi/bl-view-mcp#readme","version":"0.3.1","license":"MIT","protocols":["mcp"],"tags":["ai","black-litterman","claude","finance","mcp","optimization","portfolio"],"pricing":null,"endpoints":[{"url":"pypi:black-litterman-mcp","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","license":"pypi","repoUrl":"pypi","summary":"pypi","version":"pypi","description":"pypi","homepageUrl":"pypi"}},"derived":{"capabilities":[{"slug":"analytics.reporting","name":"Reporting & Dashboards","confidence":1,"provenance":"derived"},{"slug":"finance.markets","name":"Markets & Trading","confidence":1,"provenance":"declared"},{"slug":"dev.terminal","name":"Terminal & Shell","confidence":0.779,"provenance":"derived"}],"categories":["analytics","dev","finance"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"black-litterman-mcp","registry":"pypi","observedAt":"2026-11-02T22:25:04.810Z","publishedAt":"2025-11-24T15:34:32.929289Z","latestVersion":"0.3.1"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"black-litterman-mcp","url":"https://pypi.org/project/black-litterman-mcp/","firstSeenAt":"2026-09-09T10:27:20.750Z","fetchedAt":"2026-11-02T22:24:19.345Z","normalizedAt":"2026-11-02T22:24:19.345Z"}]},"firstSeenAt":"2026-09-09T10:27:20.750Z","updatedAt":"2026-11-02T22:25:04.810Z"}