FinanceToolkit: Transparent and Efficient Financial Analysis

by JerBouma · MCP Server · ★ 5.5k

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

How to install FinanceToolkit

pip install financetoolkit -U
uvx --from "financetoolkit[mcp]" financetoolkit-mcp-setup

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

Security audit verdict: SAFE · quality 62/100

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.

algorithmic-tradingequitiesfactor-analysisfinancefinancial-analysisfinancial-datafinancial-statementsfundamental-analysisfundamentalsmarket-data

Quick Facts

Stars5,496
Forks644
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score62/100
Last Updated2026-11-05
Created2019-04-08
Platformsmcp, python
Est. Tokens~25k

About FinanceToolkit

While browsing a variety of websites, I repeatedly observed significant fluctuations in the same financial metric among different sourc

Compatible Skills

These tools work well together with FinanceToolkit for enhanced workflows:

  • maverick-mcp — semantic(0.54)+rare_topics+same_lang+similar_pop+shared_platform (69%)
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  • quant-buddy-skills — semantic(0.23)+complementary+rare_topics+same_lang+shared_platform (52%)
  • claude-trading-skills — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)

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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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