DebtStack MCP Server

DebtStack.ai Python SDK

Corporate credit data for AI agents.

Why DebtStack?

Equity data is everywhere. Credit data isn't.

There's no "Yahoo Finance for bonds." Corporate debt structures, guarantor chains, and covenant details are buried in SEC filings—scattered across 10-Ks, 8-Ks, credit agreements, and indentures. An AI agent trying to answer "which telecom companies have leverage above 5x?" would need to read dozens of filings, extract the right numbers, and compute ratios manually.

DebtStack fixes this. We extract, normalize, and serve corporate credit data through an API built for AI agents.

Three Things You Can't Do Elsewhere

1. Cross-Company Credit Queries

# Find distressed telecom companies
GET /v1/companies?sector=Telecommunications&min_leverage=5&sort=-net_leverage_ratio

Screen ~300 companies by leverage, coverage ratios, or maturity risk in one call. No filing-by-filing analysis.

2. Pre-Built Entity Relationships

# Who guarantees this bond?
POST /v1/entities/traverse
{"start": {"type": "bond", "id": "893830AK8"}, "relationships": ["guarantees"]}

Guarantor chains, parent-subsidiary hierarchies, structural subordination—mapped and queryable. This data exists nowhere else in machine-readable form.

3. Agent-Ready Speed

< 100ms response time

AI agents chain multiple calls. If each took 30 seconds (reading a filing), a portfolio analysis would take hours. DebtStack returns in milliseconds.

Installation

pip install debtstack-ai

For LangChain integration:

pip install debtstack-ai[langchain]

Quick Start

from debtstack import DebtStackClient
import asyncio

async def main():
    async with DebtStackClient(api_key="your-api-key") as client:

        # Screen for high-leverage companies
        risky = await client.search_companies(
            sector="Telecommunications",
            min_leverage=4.0,
            fields="ticker,name,net_leverage_ratio,interest_coverage",
            sort="-net_leverage_ratio"
        )

        # Drill into the riskiest one
        ticker = risky["data"][0]["ticker"]
        bonds = await client.search_bonds(ticker=ticker, has_pricing=True)

        # Check guarantor coverage on their notes
        for bond in bonds["data"]:
            guarantors = await client.get_guarantors(bond["cusip"])
            print(f"{bond['name']}: {len(guarantors)} guarantors")

asyncio.run(main())

Synchronous Usage

from debtstack import DebtStackSyncClient

client = DebtStackSyncClient(api_key="your-api-key")
result = client.search_companies(sector="Energy", min_leverage=3.0)

What's In The Data

Pre-computed metrics: Leverage ratios, interest coverage, maturity profiles, structural subordination scores.

Relationships: Guarantor chains, issuer-entity links, parent-subsidiary hierarchies.

API Methods

Examples

Which MAG7 company has the most debt?

result = await client.search_companies(
    ticker="AAPL,MSFT,GOOGL,AMZN,NVDA,META,TSLA",
    fields="ticker,name,total_debt,net_leverage_ratio",
    sort="-total_debt",
    limit=1
)
# Returns structured data in milliseconds, not minutes

Find high-yield bonds trading at a discount

result = await client.search_bonds(
    seniority="senior_unsecured",
    min_ytm=8.0,
    has_pricing=True,
    sort="-pricing.ytm"
)

Who guarantees a specific bond?

guarantors = await client.get_guarantors("893830AK8")
for g in guarantors:
    print(f"{g['name']} ({g['entity_type']}) - {g['jurisdictio