Financial Modeling Prep MCP Server
A Model Context Protocol (MCP) implementation for Financial Modeling Prep, enabling AI assistants to access and analyze financial data, stock information, company fundamentals, and market insights.
Is Financial Modeling Prep MCP Server safe? Thin or incomplete trust evidence. Review carefully before production use.
Compare Financial Modeling Prep MCP Server
How does it stack up against its MCP Servers neighbours?
Pick any agent to compare →In detail: Financial Modeling Prep MCP Server scores 49.3/100 (Grade D), ranked #958 of 1325 tracked open-source AI agent projects, on evidence coverage B (3 of 5 independent signal types). The public evidence: no package-provenance attestation found; OSSF Scorecard rates its supply-chain practices 4.3/10; 24% of recent commits are signed; last pushed 2026-07-02. Every point is earned from checkable signals — never paid placement. How scoring works →
How Financial Modeling Prep MCP Server compares in MCP Servers
- #550 Druid MCP Server 49.4 +0.1
- #551 x64dbg MCP Server 49.4 +0.1
- #552 Financial Modeling Prep MCP Server 49.3 this agent
- #553 Home Assistant Vibecode Agent 49.3 ±0
- #554 Bgg MCP 49.1 −0.2
Bars show each HVTrust score; the tick marks Financial Modeling Prep MCP Server’s 49.3.
Where the 49.3 comes from
HVTrust dimensions vs the MCP Servers average
49.3 / 100 · 100.0% confidenceFinancial Modeling Prep MCP Server MCP Servers average (775 agents)
Quick Trust Read
How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade D reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage B is separate — it grades how many independent signal types back the score (3 of 5), so a high score on thin evidence stays visible. Full methodology →
Rank Trend
Activity & Reach
Analysis
Activity Inputs
34.4 / 100Supply Chain Trust
Common questions about Financial Modeling Prep MCP Server
Does Financial Modeling Prep MCP Server publish package provenance?
Does Financial Modeling Prep MCP Server have an OpenSSF Scorecard?
Is Financial Modeling Prep MCP Server actively maintained?
What license does Financial Modeling Prep MCP Server use?
Are Financial Modeling Prep MCP Server's commits signed?
Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.
AI agent surface
MCP, providers, tool surface
These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →
- MCP signal live
- External deps live
- Tool / plugin surface live
- Package provenance drift live
Detected changes to Financial Modeling Prep MCP Server's runtime surface and supply-chain posture, from daily public-signal snapshots. A change here means our detectors see something different — a genuinely changed capability, or better evidence of an existing one.
Maintain Financial Modeling Prep MCP Server?
For maintainers
HVTrust scores Financial Modeling Prep MCP Server from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.
Reputation Timeline
Signal history
Embed Badge Badge guide for maintainers →
For maintainers
[](https://hvtracker.net/agents/financial-modeling-prep-mcp-server)
<a href="https://hvtracker.net/agents/financial-modeling-prep-mcp-server"><img src="https://hvtracker.net/badge/financial-modeling-prep-mcp-server.svg" alt="HVTrust"></a>
Other agents in MCP Servers
GitHub REST API (repo, commits, stars, forks, license) · npm Registry (downloads, provenance) · OpenSSF Scorecard CLI
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.3 · Raw JSON