arelle-mcp

arelle-mcp

Enables LLMs to process, validate, and analyze XBRL financial filings with tools for filing operations, fact extraction, taxonomy exploration, and SEC EDGAR integration.

Category
Visit Server

README

arelle-mcp

The definitive MCP server for XBRL processing, validation, and financial analysis.

PyPI Python License

Built by King Hippopotamus.
Uses Arelle — the world's only free, open-source XBRL-certified processor — as its core engine. No other XBRL MCP server exists. This is the first.


What it does

arelle-mcp gives LLMs (Claude, GPT, etc.) full access to XBRL financial data through 17 tools:

Category Tools Description
Filing Ops xbrl_load_filing, xbrl_filing_summary, xbrl_compare_filings, xbrl_close_filing, xbrl_list_filings Load, inspect, compare, and manage XBRL/iXBRL filings
Validation xbrl_validate Validate against SEC EFM, EU ESEF, UK HMRC, or generic rules
Fact Extraction xbrl_extract_facts, xbrl_fact_details Query financial data points with filtering by concept, period, dimension, unit
Taxonomy xbrl_browse_taxonomy, xbrl_concept_details Search and explore the taxonomy (standard + company extensions)
Relationships xbrl_presentation_tree, xbrl_calculation_tree, xbrl_dimension_structure Navigate financial statement hierarchies, calculation trees, and dimensional breakdowns
SEC EDGAR xbrl_fetch_sec_filing, xbrl_search_sec_concept, xbrl_company_facts Fetch SEC filings by ticker/CIK, search historical concept data
Rendering xbrl_render_statement Reconstruct financial statements (Balance Sheet, Income Statement, Cash Flow)

Plus 5 resources (reference data) and 5 prompt templates (guided analysis workflows).


Quick Start

Install

pip install arelle-mcp

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "arelle-mcp": {
      "command": "arelle-mcp",
      "env": {
        "ARELLE_MCP_MAX_FILINGS": "5"
      }
    }
  }
}

Claude Code

claude mcp add arelle-mcp -- arelle-mcp

Cursor / Other MCP Clients

{
  "arelle-mcp": {
    "command": "python",
    "args": ["-m", "arelle_mcp"]
  }
}

HTTP Mode (Remote)

ARELLE_MCP_TRANSPORT=streamable-http ARELLE_MCP_PORT=8000 arelle-mcp

Usage Examples

Analyze Apple's Latest 10-K

"Fetch Apple's latest 10-K and give me a financial summary"

The LLM will:

  1. Call xbrl_fetch_sec_filing(ticker="AAPL", filing_type="10-K")
  2. Extract key metrics with xbrl_extract_facts
  3. Render financial statements with xbrl_render_statement

Validate a Filing

"Validate this SEC filing: https://www.sec.gov/Archives/edgar/data/..."

Compare Two Quarters

"Compare Apple's Q2 and Q3 2024 10-Q filings"

Historical Revenue Trend

"Show me Microsoft's revenue history from SEC EDGAR"

The LLM calls xbrl_search_sec_concept(cik="789019", concept="Revenues") — no filing load needed.


Architecture

Core Design Decisions

1. Single-Session Lock — Arelle uses global state that isn't thread-safe. All operations are serialized via asyncio.Lock and offloaded to a ThreadPoolExecutor to avoid blocking the MCP event loop.

2. LRU Filing Cache — Each loaded filing consumes 30-60MB. An OrderedDict-based LRU cache (default: 5 filings) automatically evicts the oldest filing when capacity is reached, calling model.close() to free memory.

3. Lazy Imports — Arelle is heavy (~200MB with taxonomies). All Arelle imports happen lazily inside tool functions, keeping server startup fast.

Project Structure

src/arelle_mcp/
├── server.py            # FastMCP instance, lifespan, registration
├── arelle_wrapper.py    # ArelleManager — session lifecycle, concurrency, caching
├── serializers.py       # Arelle objects → JSON/markdown
├── constants.py         # Arcroles, disclosure systems, SEC config
├── tools/
│   ├── filing.py        # Load, summary, compare, close, list
│   ├── validation.py    # Validate against disclosure systems
│   ├── facts.py         # Extract and filter facts
│   ├── taxonomy.py      # Browse concepts, get details
│   ├── relationships.py # Presentation, calculation, dimension trees
│   ├── edgar.py         # SEC EDGAR API integration
│   └── rendering.py     # Financial statement rendering
├── resources/           # Reference data (disclosure systems, common concepts)
└── prompts/             # Guided analysis workflow templates

Configuration

Environment Variable Default Description
ARELLE_MCP_MAX_FILINGS 5 Max filings cached in memory
ARELLE_MCP_CACHE_DIR (none) Directory for taxonomy cache
ARELLE_MCP_TRANSPORT stdio Transport: stdio or streamable-http
ARELLE_MCP_PORT 8000 HTTP port (when using streamable-http)
ARELLE_MCP_LOG_LEVEL INFO Logging level

Development

git clone https://github.com/TheKingHippopotamus/Arelle-MCP.git
cd Arelle-MCP
pip install -e ".[dev]"

# Run tests
pytest

# Type check
mypy src/arelle_mcp

# Lint
ruff check src/

# Test with MCP Inspector
npx @modelcontextprotocol/inspector arelle-mcp

Supported File Formats

  • XBRL Instance Documents (.xbrl, .xml)
  • Inline XBRL (.htm, .html) — SEC mandated since June 2021
  • iXBRL Document Sets
  • ZIP archives containing XBRL
  • SEC EDGAR URLs (auto-fetched)
  • Taxonomy Packages (.zip)

Supported Disclosure Systems

  • SEC EFM — US Securities and Exchange Commission
  • ESEF — European Single Electronic Format (EU/ESMA)
  • HMRC — UK HM Revenue & Customs
  • GFM — Global Filing Manual

License

Apache 2.0 — same as Arelle itself.


Built by King Hippopotamus — with zero compromises.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured