lunchmoney-mcp: A Model Context Protocol server implementation for LunchMoney, providing programmatic access to personal

by akutishevsky · MCP Server · ★ 112

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

Security audit verdict: SAFE · quality 76/100

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.

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.

dxtlunchmoneylunchmoney-apimcpmcp-servermodel-context-protocol

Quick Facts

Stars112
Forks19
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score76/100
Open Issues1
Last Updated2026-08-03
Created2025-07-27
Platformsmcp, node
Est. Tokens~17k

About lunchmoney-mcp

LunchMoney MCP Server A Model Context Protocol (MCP) server implementation for LunchMoney, providing programmatic access to personal finance management through LunchMoney's API. Also available as an MCP Bundle (.mcpb) for easy installation in Claude Desktop. Heads up — v2.0.0 is a breaking release. This server now targets LunchMoney's v2 API (, currently in alpha).

Compatible Skills

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

  • yuque-mcp-server — semantic(0.62)+same_lang+similar_pop+shared_platform (57%)
  • investor-agent — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)
  • mcp-server — semantic(0.62)+same_lang+similar_pop+shared_platform (52%)
  • airtable-mcp-server — semantic(0.47)+same_lang+similar_pop+shared_platform (51%)

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  • airtable-mcp-server by domdomegg · ⭐ 455

    🗂️🤖 Airtable Model Context Protocol Server, for allowing AI systems to interact with your Airtable bases

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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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