copilot-money-mcp: MCP server for Copilot Money App - AI powered personal finance queries using local data

by ignaciohermosillacornejo · MCP Server · ★ 85

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install copilot-money-mcp

npm install -g copilot-money-mcp

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 81/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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Quick Facts

Stars85
Forks30
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score81/100
Open Issues14
Last Updated2026-10-21
Created2026-01-11
Platformsmcp, node
Est. Tokens~17k

About copilot-money-mcp

Copilot Money MCP Server Query and manage your personal finances with AI using local Copilot Money data Disclaimer This is an independent, community-driven project and is not affiliated with, endorsed by, or associated with Copilot Money or its parent company in any way. This tool was created by an independent developer to enable AI-powered queries of locally cached data.

Compatible Skills

These tools work well together with copilot-money-mcp for enhanced workflows:

  • quanta-quest — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • openpaw — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • github-copilot-api-vscode — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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