financetoolkit
PyPI
v2.2.0
Published by an unidentified publisher — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
Transparent and Efficient Financial Analysis
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 6.3 = 94. What the published surface and source actually contain:
| Points | What was found | Category |
|---|---|---|
| −6.3 | Unsafe deserialization MTC-SRC-007 | permissions |
2. Client adoption risk — 94 − 8 = 86. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −6 | capability blast radius (high) — client exposure if the model is manipulated |
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
In the server's implementation (`financetoolkit/ratios/ratios_controller.py:495`): Evaluating strings as code is the most direct RCE primitive; if any tool input reaches it, the server executes attacker-chosen code. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: calculation = eval(formula_adjusted) # noqa total_financials.loc[:, name, :] = calculation.a
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server financetoolkit/ratios/ratios_controller.py
In the server's implementation (`financetoolkit/mcp_server/mcp_controller.py:352`): Spawning a shell/process is command-execution capability; with unsanitized tool input it is command injection / RCE. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: ) sys.exit( subprocess.call( # noqa ["npx", "@modelcontextprotocol/inspector", "python", server
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server financetoolkit/mcp_server/mcp_controller.py
In the server's implementation (`financetoolkit/cache/serialization_model.py:50`): Deserializing untrusted data with these APIs can execute arbitrary code (a well-known RCE gadget class). This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: ect. """ return pickle.loads(zlib.decompress(payload)) # noqa: S301 def encode_object(value: Any) -> bytes:
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server financetoolkit/cache/serialization_model.py
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v2.2.0 latest |
B 86/100 | 3 | 1.13.0 | 2026-08-25 |
v2.1.4 |
B 86/100 | 4 | 1.12.1 | 2026-07-27 |
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
npx mcptrustchecker scan financetoolkit --online --registry pypi
Security scan results for the A5sql MCP server.
Expose AgentsKit tools as an MCP server — use them from Claude Desktop, Cursor, Windsurf, or any MCP host.
MCP server for Aiven cloud data platform - manage PostgreSQL, Kafka, and other services
MCP Server for All-Inkl.com hosting via KAS API
Structured aquarium, marine, terrarium and paludarium data for AI agents.
MCP server that gives your AI agent real vehicle data — specs, representative images, federal recalls, owner complaints, service bulletins, defect investigations, and OBD-II DTC reference — from the CarVector API.