SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server
SEC XBRL financial data.
LlamaIndex agents combine SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
4 Tools
- Key Financials — Revenue, income, assets, EPS, cash
- Financial Metric — Any US-GAAP concept
- All Facts — Complete XBRL data dump
- Industry Comparison — Cross-company metric frames
Zero Auth
Like a free Bloomberg terminal
The SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to LlamaIndex via MCP
Follow these steps to integrate the SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 4 tools from SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison
Why Use LlamaIndex with the SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server
LlamaIndex provides unique advantages when paired with SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison tools were called, what data was returned, and how it influenced the final answer
SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server delivers measurable value.
Hybrid search: combine SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison for fresh data
Analytical workflows: chain SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison queries with LlamaIndex's data connectors to build multi-source analytical reports
SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to LlamaIndex via MCP:
get_all_company_facts
This is the raw, comprehensive dataset — hundreds of concepts across multiple years. Use get_key_financials for a curated summary, or this for deep analysis. Get ALL XBRL financial facts for a company — complete financial data dump
get_financial_metric
Common concepts: Revenues, NetIncomeLoss, Assets, Liabilities, StockholdersEquity, EarningsPerShareBasic, LongTermDebt, ResearchAndDevelopmentExpense, CashAndCashEquivalentsAtCarryingValue, CommonStockSharesOutstanding. If the concept is not found, returns available concepts. Get a specific US-GAAP financial concept for a company (e.g., Revenue, Debt, R&D)
get_industry_comparison
Useful for industry comparison and screening. Example: get all companies' Revenue for CY2024. Period format: CY2024 (annual), CY2024Q1 (quarterly), CY2024Q1I (instant). Compare a financial metric across ALL companies — industry-wide XBRL frame data
get_key_financials
Returns the most recent 5 reported values across 10-K and 10-Q filings. This is like a mini Bloomberg terminal — for free. Get key financial data for a company — revenue, net income, assets, equity, EPS, cash
Example Prompts for SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison immediately.
"Get Apple's key financial data — revenue, income, assets, and EPS"
"What is Meta's exact Research and Development Expense?"
"Show me a comparison of Revenue across all companies for CY2024"
Troubleshooting SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server with LlamaIndex
Common issues when connecting SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison + LlamaIndex FAQ
Common questions about integrating SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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Connect SEC EDGAR Financials — Revenue, Income, Assets, EPS & Industry Comparison to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.
