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catalog / Finance / Financial Datasets MCP Server
◇MCP serverFinanceFree

Financial Datasets MCP Server

MCP server for the Financial Datasets API — company financials, stock prices, and crypto data.

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Installationen236
↗ Quell-Repository
← More FinanceFinance leaderboard →How we grade security →Source ↗

Financial Datasets MCP Server

This Model Context Protocol server provides access to stock-market and financial data from the Financial Datasets API, letting AI assistants retrieve company financials directly.

Its tools include income statements, balance sheets, and cash-flow statements, current and historical stock prices, and company news, plus crypto support (available tickers and current/historical crypto prices). An API key from Financial Datasets is required.

It is intended for developers and analysts who want their agent to pull fundamental and price data for equities and crypto.

Rating rank
#1
of 15 in Finance
Install rank
#15
of 15 in Finance
Security score
100/100 · A
safe
Security rank
#1
of 15 in Finance
Installs
236
cat avg 85k
This listing vs category average
Installs
this
cat avg
Security (of 100)
this
cat avg
Adoption trend
See the Finance leaderboard →
✓ Security: Safe · 100100/100 · grade Ascanned 36m ago
✓ no compromise signals5 risk-surface · 5/20 OWASP controls flagged

Only compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score. Dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to the OWASP control it belongs to below.

What this capability can do · high confidence (static)
Tools (11)
get_income_statementsget_balance_sheetsget_cash_flow_statementsget_current_stock_priceget_historical_stock_pricesget_company_newsget_available_crypto_tickersget_crypto_pricesget_historical_crypto_pricesget_current_crypto_priceget_sec_filings
⚑ network⚑ secrets
egress → www.financialdatasets.ai, astral.sh, claude.ai, api.financialdatasets.ai

Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not.

OWASP Top 10 for LLM Applications
⚠LLM03Supply Chaincritical
Vulnerable/compromised dependencies, models or archives in the artifact.
•Vulnerable dependencies — 19 known vulnerabilities in: h11@0.14.0, idna@3.10, mcp@1.3.0, pygments@2.19.1, python-dotenv@1.0.1, starlette@0.46.0 (CWE-1395)risk surface
⚠LLM05Improper Output Handlinghigh
Code that pipes model/user output into shell, eval, SQL or paths unsafely.
•Suspicious code patterns — pipe-to-shell install · financial-datasets-mcp-server-08e7a3d/README.md (CWE-494)risk surface
⚠LLM06Excessive Agencylow
Over-broad tool/permission surface or unrestricted egress.
•External endpoints declared — 1 distinct host(s) · financial-datasets-mcp-server-08e7a3d/.env.examplerisk surface
•External endpoints declared — 3 distinct host(s) · financial-datasets-mcp-server-08e7a3d/.gitignorerisk surface
•External endpoints declared — 4 distinct host(s) · financial-datasets-mcp-server-08e7a3d/README.mdrisk surface
§LLM09MisinformationGovernance
Artifacts designed to produce false/deceptive output.
Detectable only by runtime behavioral evaluation; addressed via responsible-use attestation.
◷LLM10Unbounded ConsumptionRuntime-enforced
Unbounded loops/recursion causing DoS or runaway cost.
Enforced at runtime by the gateway (rate limits + spend caps + size caps); static check flags unbounded loops.
✓LLM01Prompt InjectionPassed
✓LLM02Sensitive Information DisclosurePassed
✓LLM04Data and Model PoisoningPassed
Backdoors/poisoning in training data or serialized models.
Behavioral poisoning needs model execution; static check covers unsafe serialization + dataset skew only.
✓LLM07System Prompt LeakagePassed
✓LLM08Vector and Embedding WeaknessesPassed
PII or plaintext source leakage in embedding/vector exports.
Embedding inversion/poisoning is largely runtime; static check covers PII in vector exports.
OWASP Machine Learning Security Top 10
⚠ML06AI Supply Chaincritical
Compromised PyPI/npm packages, typosquats, unsafe serialized models.
•Vulnerable dependencies — 19 known vulnerabilities in: h11@0.14.0, idna@3.10, mcp@1.3.0, pygments@2.19.1, python-dotenv@1.0.1, starlette@0.46.0 (CWE-1395)risk surface
⚠ML09Output Integrityhigh
Middleware tampering with model outputs in transit.
Gateway enforces TLS + response integrity; static check flags output-rewriting code.
•Suspicious code patterns — pipe-to-shell install · financial-datasets-mcp-server-08e7a3d/README.md (CWE-494)risk surface
§ML01Input Manipulation (Adversarial)Governance
Models vulnerable to adversarial perturbations.
Requires runtime robustness evaluation; addressed via publisher robustness attestation.
§ML03Model InversionGovernance
Training data reconstructable from a model's outputs.
Runtime/evaluation property; addressed via model-card data-provenance + DP attestation.
§ML04Membership InferenceGovernance
Determining whether a record was in the training set.
Runtime/evaluation property; addressed via overfitting disclosure + DP attestation.
§ML08Model SkewingGovernance
Models trained on skewed data producing biased output.
Requires fairness evaluation; addressed via model-card bias/limitations disclosure.
✓ML02Data PoisoningPassed
Poisoned training datasets with triggers or anomalous distributions.
Static check covers trigger phrasing, PII and label skew; full poisoning detection is runtime.
✓ML05Model TheftPassed
Unlicensed re-distribution / license-incompatible derivatives.
Static check verifies license declaration; extraction throttling is runtime.
✓ML07Transfer Learning AttackPassed
Backdoored base models / LoRA adapters propagating to derivatives.
Backdoor detection needs behavioral probing; static check covers unsafe serialization + provenance.
✓ML10Model Poisoning (Weights)Passed
Tampered model weight files; integrity must be verifiable.
Static check enforces safe formats + records a content hash for downstream verification.
Other findings (3) · hygiene / uncategorized
•Unrecognized file type — '.gitignore' is not on the allowlist · financial-datasets-mcp-server-08e7a3d/.gitignorerisk surface
•Unrecognized file type — '.python-version' is not on the allowlist · financial-datasets-mcp-server-08e7a3d/.python-versionrisk surface
•Unrecognized file type — '.?' is not on the allowlist · financial-datasets-mcp-server-08e7a3d/LICENSErisk surface
✔ verified source · pinned financial-datasets-mcp-server-08e7a3d · changed since last scan · +egress www.financialdatasets.ai, astral.sh, claude.ai
Check against a policy

The same gate an agent runs before installing (POST /api/v1/trust/financial-datasets-mcp/check). Click a policy:

Consume Financial Datasets MCP Server programmatically. Authenticate with an API key or session — see Authorize an agent.

# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/financial-datasets-mcp

# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/financial-datasets-mcp/check \
  -H "Content-Type: application/json" \
  -d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'

# CLI
npx ai-supply add financial-datasets-mcp

# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/financial-datasets-mcp/install \
  -H "Authorization: Bearer $AIM_KEY"

# MCP tool
install_listing({ "slug": "financial-datasets-mcp" })
OpenAPI spec →
vlatest
✓ Security: Safe · 1005h ago

Curated mirror — latest upstream source. See the repository for tagged releases.

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