edgar-mcp
This server provides AI models with access to SEC EDGAR for public company filings, financial data, and XBRL facts. It includes tools for company lookup, filing retrieval, XBRL data extraction, and full-text search, with caching and rate limiting.
Company Lookup (
lookup_company): Resolve ticker, CIK, or company name to canonical EDGAR identity; ambiguous names return candidates.Filing History (
list_filings): List filings with form type and date range filters; handles over 1000 filings.Filing Text (
get_filing_text): Fetch filing text by URL, with windowing and stripped iXBRL.XBRL Concepts (
list_concepts): List XBRL tags reported by a company, sorted by frequency.XBRL Time Series (
get_concept): Get time series for a specific XBRL concept.Company Comparison (
compare_concept): Rank companies by an XBRL concept for a period.Full-Text Search (
search_filings): Search EDGAR filings from 2001 onward, with filters.Cache Stats (
cache_stats): Monitor cache hit rate, request count, and bytes downloaded.
Notable behaviors:
Auto-rate-limits to SEC's 10 req/s, even under parallel bursts.
Caches per-host according to freshness rules (immutable Archives, 1-hour TTL for data.sec.gov); use
ttl=0to bypass cache.Requires Python 3.11+.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@edgar-mcp Show me Apple's latest 10-K text"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
edgar-mcp
An MCP server that gives a model working access to SEC EDGAR — company filings, filing text, and XBRL financial facts.
Verification launch kit
Inspect | Published evidence |
Strongest result | Warm 10-K reads reach 1.1 ms / 0.00 MB, measured at 138x the cold fetch |
Verification | Wire-level request windows, transferred-byte counters, offline fixtures, and burst tests |
Failure boundary | Public EDGAR data only; not an investment recommendation or a complete accounting model |
Reproduce |
|
Interactive replay |
Evidence contract: a class named
RateLimiterorCacheproves nothing by itself. The benchmark measures grants inside a real one-second window and bytes transferred on the wire.
→ Interactive results page — fire a burst of tool calls and watch a full token bucket sail through the limit it was written to enforce, then see what each EDGAR host can actually validate.
EDGAR will happily hand you a 9 MB filing and then throttle you for asking twice. The interesting part of this server is everything between the model and the wire.
Related MCP server: SEC EDGAR MCP
What is actually implemented
ticker, CIK, or company name resolves to an EDGAR identity, with ambiguous names returning candidates instead of a silent wrong guess;
filing history spans EDGAR's overflow files, so companies past ~1000 filings don't get silently truncated to the recent page;
filing text arrives windowed with a
next_offsetcursor — a 10-K is ~206K characters and does not belong in a context window whole;inline-XBRL scaffolding is stripped, so extracted text starts at the prose and not at 11K characters of
false2025FY0000320193...;XBRL concepts come back as a time series, with a tag-discovery tool because nobody knows the right us-gaap tag name off the top of their head;
one metric can be ranked across every filer for a period via the frames API;
full-text search covers filings from 2001 onward;
requests are paced under SEC's 10 req/s ceiling even when a model fires a parallel burst of tool calls;
responses are cached per-host by the freshness rule that host actually supports.
flowchart LR
M["model tool call"] --> R["resolve ticker / CIK / name"]
R --> C{"cached?"}
C -->|"Archives: immutable"| D["disk, no network"]
C -->|"data.sec.gov: TTL fresh"| D
C -->|"stale or absent"| P["pacer @ 9 req/s"]
P --> E["EDGAR"]
E -->|"429 / 5xx"| P
E --> W["cache write"]
W --> X["iXBRL strip + window"]
D --> X
X --> MMeasured, not implied
Apple M-series, macOS, Python 3.13, live EDGAR. Reproduce with make bench.
Check | Result |
Tests passing | 32 |
| 3.75 MB / 97 ms |
| 0.00 MB / 1.4 ms (68×) |
10-K document cold fetch | 1.52 MB / 154 ms |
10-K document warm read | 0.00 MB / 1.1 ms (138×) |
| 0.00 MB / 0.2 ms (755×) |
Sustained request rate | 9.2 req/s |
Peak requests in any 1s window | 10 (SEC ceiling: 10) |
iXBRL scaffolding removed from a 10-K | 11,303 chars |
Text extraction throughput | 23.4 MB/s |
Windows to read a full 10-K @ 40K | 6 |
Both the cache and pacer numbers above are post-fix. The first benchmark run reported a 3.75 MB "cache hit" that was really a full re-download, and 19 requests inside a one-second window against a 10 req/s limit. See DESIGN.md.
Where it loses
Freshness on
data.sec.govis a guess. That host sends noETagand noLast-Modified, so conditional requests are impossible and freshness falls back to a 1-hour TTL. A filing that lands mid-TTL is invisible until it expires. Passttl=0if you need read-your-writes.Windowing re-reads, it doesn't range-read. EDGAR honors HTTP
Rangeon Archives (verified:206,accept-ranges: bytes), but partial HTML can't be parsed reliably, so the whole document is fetched once and windowed from cache. The first call on a large filing pays the full download.Search is never cached. Results are query-shaped and EDGAR's full-text endpoint offers no validators, so every search is a live round trip against the 9 req/s budget.
The pacer is global. Ten companies queried in parallel serialize at ~9 req/s. Correct, but not fast.
XBRL values are consolidated totals. Dimensional breakdowns (by segment, by geography) exist in the data and this does not surface them.
Full-text search starts at 2001. Older filings are reachable through
list_filings, notsearch_filings.
Verify it
make test # 32 tests, no network
make bench # live EDGAR, prints the table aboveSetup
SEC requires a User-Agent carrying a real contact address and blocks requests without one. The server refuses to start rather than letting you discover that as a confusing 403 later.
export EDGAR_USER_AGENT="your-project you@example.com"Build and run the local container over stdio:
docker build -t edgar-mcp:local .
docker run --rm -i \
-e EDGAR_USER_AGENT="your-project you@example.com" \
edgar-mcp:localThe image runs as an unprivileged user and writes its EDGAR cache under that
user's home directory. Mount /home/edgar/.cache/edgar-mcp if the cache should
survive container restarts.
Add to claude_desktop_config.json or .mcp.json:
{
"mcpServers": {
"edgar": {
"command": "uv",
"args": ["run", "--directory", "/path/to/edgar-mcp", "edgar-mcp"],
"env": { "EDGAR_USER_AGENT": "your-project you@example.com" }
}
}
}Tools
Tool | Purpose |
| ticker / CIK / name → EDGAR identity |
| filing history, filtered by form and date |
| windowed document text, follow |
| which XBRL tags a company actually reports |
| time series for one tag |
| one tag ranked across all filers for a period |
| full-text search, 2001→ |
| hit rate, requests, bytes downloaded |
A typical chain is lookup_company → list_filings → get_filing_text, or
list_concepts → get_concept when you want numbers rather than prose.
License
MIT
Available Tools
8 toolscache_statsB
Cache hit rate, request count, and bytes downloaded this session.
Inputs: no named parameters.
compare_conceptB
Compare one XBRL concept across all filers for a period, ranked by value. Period is CY2023 (annual), CY2023Q1 (duration), or CY2023Q1I (instant).
Inputs: tag (required), period (required), unit, limit, +1 more.
get_conceptC
Time series for one XBRL concept (e.g. Revenues, Assets, NetIncomeLoss) as reported by a company across filings.
Inputs: tag (required), query (required), limit, taxonomy.
get_filing_textA
Fetch the text of a filing document from its EDGAR Archives URL. Returns a window; if truncated is true, call again with next_offset.
Inputs: url (required), limit, offset.
list_conceptsA
List the XBRL tags a company actually reports, most-reported first. Use this to find the right tag before calling get_concept.
Inputs: query (required), contains.
list_filingsA
List a company's filings, newest first. Filter by form type (10-K, 8-K, DEF 14A, 4, ...) and filing-date range (YYYY-MM-DD).
Inputs: query (required), forms, limit, since, +1 more.
lookup_companyA
Resolve a ticker, CIK, or company name to its EDGAR identity.
Inputs: query (required).
search_filingsA
Full-text search across EDGAR filings from 2001 onward. Returns matching filings with document URLs.
Inputs: q (required), forms, limit, since, +1 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 8 tools
Each tool targets a distinct purpose: listing concepts, resolving companies, listing filings, fetching filing text, retrieving concept time series, searching full-text, comparing concepts, and cache statistics. No two tools appear to overlap in function, making selection unambiguous.
Most tools follow the verb_noun pattern (list_concepts, lookup_company, get_filing_text, search_filings, compare_concept). The only deviation is cache_stats, which uses a noun_noun form and lacks a verb, but this is a minor inconsistency in an otherwise coherent naming scheme.
With 8 tools, the server is well-scoped for SEC EDGAR data access. Each tool covers a necessary function without redundancy, and the count fits comfortably within the ideal range for a domain-specific MCP server.
The tool surface provides a complete read-only workflow: company resolution, filing discovery, full-text search, XBRL concept retrieval, and cross-company comparison. No obvious gaps exist for typical EDGAR use cases, and cache_stats adds operational insight.
Maintenance
Related MCP Connectors
Read, search, and compare SEC EDGAR filings, pull tables, and run AI analysis from any MCP client.
EDGAR MCP — SEC EDGAR public APIs (free, no auth)
SEC MCP — SEC EDGAR public APIs (free, no auth)
SEC XBRL MCP — wraps SEC EDGAR XBRL API (data.sec.gov)
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