Financial-Modeling-Prep-MCP-Server: A Model Context Protocol implementation for Financial Modeling Prep, enabling AI assistants to access

by imbenrabi · MCP Server · ★ 149

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

How to install Financial-Modeling-Prep-MCP-Server

npm install -g financial-modeling-prep-mcp-server

Taken from the project's README on GitHub.

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.

aietf-investmentsfinancial-modeling-prepfmpllmmcpmcp-servermcp-toolsstock-datastock-market

Quick Facts

Stars149
Forks55
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score76/100
Open Issues1
Last Updated2026-11-05
Created2025-05-22
Platformsmcp, node
Est. Tokens~15k

About Financial-Modeling-Prep-MCP-Server

Financial Modeling Prep MCP Server A Model Context Protocol (MCP) server for the Financial Modeling Prep API, exposing 250+ financial data tools to AI assistants. Features 250+ Financial Tools across 24 categories — stocks, ETFs, crypto, forex, commodities, economics, and more Dynamic Tool Management — built on toolception for runtime enable/disable of toolsets via meta-tools Three Server Modes —...

Compatible Skills

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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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