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SEC EDGAR MCP Server

by openpharma-org
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Unofficial SEC EDGAR MCP Server

A Model Context Protocol (MCP) server that provides comprehensive access to the U.S. Securities and Exchange Commission's EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system. This server enables AI assistants and applications to search, retrieve, and analyze public company filings, financial statements, and dimensional XBRL data from the SEC's database.

Key Features

  • Company Discovery: Find companies by name or ticker with real-time SEC data

  • Complete Filing Access: Full company submission histories and document details

  • Advanced XBRL Analysis: Extract dimensional financial facts with geographic/segment breakdowns

  • Intelligent Fact Search: Find specific financial values with dimensional context

  • Business Intelligence: Automated fact classification and table generation

  • Multi-API Integration: Robust fallback mechanisms across SEC endpoints

  • Real-time Data: Direct access to SEC's live EDGAR database

  • MCP Compatible: Works seamlessly with Cursor, Claude Desktop, and other MCP clients

European Filings - Sister Project

Looking for European company financial data? Check out our companion server:

EU Filings MCP Server - Access financial filings from 27+ EU countries via ESEF (European Single Electronic Format)

98% feature parity with this SEC server, optimized for European regulatory frameworks.

Usage

{ "mcpServers": { "sec-mcp-server": { "command": "node", "args": ["/path/to/sec-mcp-server/build/index.js"], "env": {} } } }

Dimensional XBRL Capabilities

Fact Table Generation

Extract precise financial facts with complete dimensional context:

{ "method": "build_fact_table", "cik_or_ticker": "JNJ", "target_value": 638000000, "tolerance": 50000000 }

Returns dimensional facts like:

  • $638.0M = J&J Electrophysiology Non-US Revenue (Q1 2025)

  • Complete dimensional breakdown: Geography + Business Segment + Subsegment

  • Full XBRL context: us-gaap:NonUsMember + jnj:MedTechMember + jnj:ElectrophysiologyMember

Business Intelligence Extraction

Automatically classifies and analyzes financial facts:

  • Subsegment Revenue: Product-line specific performance

  • Geographic Revenue: International vs domestic breakdowns

  • Segment Revenue: Business division analysis

  • Comparative Analysis: Cross-product and cross-geography insights

Complete API Reference

The server provides a unified sec_edgar tool with 10 powerful methods:

Core Company Operations

1. Search Companies (search_companies)

Find companies by name or ticker using SEC's official database.

{ "method": "search_companies", "query": "Johnson & Johnson" }

2. Get Company CIK (get_company_cik)

Convert ticker symbols to Central Index Keys with validation.

{ "method": "get_company_cik", "ticker": "JNJ" }

3. Get Company Submissions (get_company_submissions)

Retrieve complete filing history with enhanced metadata.

{ "method": "get_company_submissions", "cik_or_ticker": "0000200406" }

Financial Data Access

4. Get Company Facts (get_company_facts)

Access all XBRL financial data with structured organization.

{ "method": "get_company_facts", "cik_or_ticker": "JNJ" }

5. Get Company Concept (get_company_concept)

Extract specific financial concepts with historical trends.

{ "method": "get_company_concept", "cik_or_ticker": "JNJ", "taxonomy": "us-gaap", "tag": "RevenueFromContractWithCustomerExcludingAssessedTax" }

6. Get Frames Data (get_frames_data)

Analyze aggregated data across companies and periods.

{ "method": "get_frames_data", "taxonomy": "us-gaap", "tag": "Assets", "unit": "USD", "frame": "CY2024Q1I" }

Advanced Dimensional Analysis

7. Get Dimensional Facts (get_dimensional_facts)

Extract facts with complete dimensional context from XBRL instance documents.

{ "method": "get_dimensional_facts", "cik_or_ticker": "JNJ", "accession_number": "0000200406-25-000119", "search_criteria": { "concept": "RevenueFromContractWithCustomerExcludingAssessedTax", "valueRange": { "min": 588000000, "max": 688000000 }, "dimensions": { "subsegment": "Electrophysiology" } } }

8. Search Facts by Value (search_facts_by_value)

Find financial facts around specific target values with filters.

{ "method": "search_facts_by_value", "cik_or_ticker": "JNJ", "target_value": 638000000, "tolerance": 50000000, "filters": { "concept": "Revenue", "formType": "10-Q" } }

9. Build Fact Table (build_fact_table)

Generate comprehensive dimensional fact tables with business intelligence.

{ "method": "build_fact_table", "cik_or_ticker": "JNJ", "target_value": 638000000, "tolerance": 50000000, "options": { "maxRows": 25, "showDimensions": true, "sortBy": "deviation" } }

Utility Operations

10. Filter Filings (filter_filings)

Enhanced filtering with date ranges and form types.

{ "method": "filter_filings", "filings": [...], "form_type": "10-Q", "start_date": "2024-01-01", "end_date": "2024-12-31" }

🏗️ Enhanced Architecture

Multi-Tier API Access Strategy

  1. Primary: Direct iXBRL document parsing from EDGAR Archives

  2. Secondary: SEC Submissions API for filing discovery and metadata

  3. Tertiary: Company Facts API with dimensional inference

  4. Emergency: Known dimensional structure mapping

SEC API Compliance

  • Official Endpoints: Uses data.sec.gov APIs per SEC guidelines

  • Proper User-Agent: SEC-Research-Tool/1.0 (contact@research.org)

  • Rate Limiting: Respects 10 requests/second SEC limit

  • Error Recovery: Graceful degradation with meaningful diagnostics

iXBRL Parser Technology

  • Modern Format Support: Handles Inline XBRL (HTML-embedded) instead of legacy XML

  • Dimensional Extraction: Parses <ix:nonFraction>, <ix:fraction>, and context relationships

  • Business Classification: Automatically categorizes facts by type and dimensional scope

  • Context Resolution: Maps XBRL contexts to readable dimensional breakdowns

Real-World Use Cases

Investment Analysis

{ "method": "build_fact_table", "cik_or_ticker": "AAPL", "target_value": 100000000000, "tolerance": 10000000000 }

Find all facts around $100B for Apple with dimensional context

Competitive Intelligence

{ "method": "search_facts_by_value", "cik_or_ticker": "TSLA", "target_value": 20000000000, "filters": { "concept": "Revenue", "dimensions": {"geography": "International"} } }

Analyze Tesla's international revenue performance

Regulatory Compliance Monitoring

{ "method": "get_dimensional_facts", "cik_or_ticker": "JPM", "search_criteria": { "concept": "LoanLossProvision", "valueRange": {"min": 1000000000, "max": 5000000000} } }

Monitor JPMorgan's loan loss provisions with risk segmentation

Cross-Company Benchmarking

{ "method": "get_frames_data", "taxonomy": "us-gaap", "tag": "OperatingIncomeLoss", "unit": "USD", "frame": "CY2024Q3I" }

Compare operating income across all companies for Q3 2024

SEC Filing Reference

Major Form Types

Form

Description

Frequency

Key Data

10-K

Annual Report

Yearly

Complete financials, business overview

10-Q

Quarterly Report

Quarterly

Unaudited financials, interim updates

8-K

Current Report

As needed

Material events, acquisitions

DEF 14A

Proxy Statement

Annually

Executive compensation, voting matters

20-F

Foreign Annual

Yearly

Non-US company annual report

S-1

Registration

As needed

IPO registration statement

XBRL Taxonomies

US-GAAP (us-gaap)

Primary financial concepts:

  • Assets - Total company assets

  • Liabilities - Total liabilities

  • StockholdersEquity - Shareholders' equity

  • RevenueFromContractWithCustomerExcludingAssessedTax - Revenue excluding taxes

  • NetIncomeLoss - Net income or loss

  • OperatingIncomeLoss - Operating income or loss

  • CashAndCashEquivalents - Cash and equivalents

Dimensional Axes

  • srt:StatementGeographicalAxis - Geographic segmentation

  • us-gaap:StatementBusinessSegmentsAxis - Business segment breakdown

  • us-gaap:SubsegmentsAxis - Product line subsegments

  • us-gaap:StatementEquityComponentsAxis - Equity components

Common Members

  • Geography: us-gaap:UsMember, us-gaap:NonUsMember

  • Business: *:TechnologyMember, *:HealthcareMember, *:MedTechMember

  • Products: *:ElectrophysiologyMember, *:OrthopedicsMember

Advanced Query Patterns

Finding Dimensional Revenue Facts

{ "method": "get_dimensional_facts", "cik_or_ticker": "JNJ", "search_criteria": { "concept": "RevenueFromContractWithCustomerExcludingAssessedTax", "dimensions": { "us-gaap:StatementBusinessSegmentsAxis": "jnj:MedTechMember", "us-gaap:SubsegmentsAxis": "jnj:ElectrophysiologyMember" } } }

Building Comprehensive Analysis Tables

{ "method": "build_fact_table", "cik_or_ticker": "PFE", "target_value": 15000000000, "tolerance": 2000000000, "options": { "maxRows": 50, "sortBy": "value", "filters": { "concept": "Revenue", "formType": "10-Q" } } }

Cross-Period Comparison

{ "method": "search_facts_by_value", "cik_or_ticker": "AMZN", "target_value": 50000000000, "tolerance": 5000000000, "filters": { "concept": "OperatingIncome" } }

Performance Optimization

Best Practices

  1. Use CIK instead of ticker when possible for faster lookups

  2. Cache Company Facts data for repeated concept queries

  3. Limit fact table rows with maxRows option for large datasets

  4. Use specific accession numbers to avoid submission lookups

  5. Batch similar requests to respect rate limits

Resources

Important: This is an unofficial tool. Please respect SEC's data usage guidelines and terms of service. Always verify critical financial data through official SEC sources.

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