Passive domain intelligence from public sources. Queries DNS records, Microsoft/Google identity endpoints, and certificate transparency logs to build a picture of an organization's technology stack — no credentials, no API keys, no active scanning.
Drop in a domain, get a calibrated read on its identity stack, email posture, and cloud footprint in seconds.
Defensive use only. recon is designed for legitimate security posture assessment, IT architecture review, vendor due diligence, and defensive hardening. It performs zero active scanning and zero credentialed access. See docs/legal.md for the full intended-use policy.
recon contoso.comContoso Ltd
contoso.com
──────────────────────────────────────────────────────────────────────────────
Provider Microsoft 365 (primary) via Proofpoint gateway + Google Workspace (secondary)
Tenant a1b2c3d4-e5f6-7890-abcd-ef1234567890 • NA
Auth Federated (Entra ID + Google Workspace)
Confidence ●●● High (4 sources)
Services
Email Microsoft 365, Google Workspace, Proofpoint, DMARC, DKIM,
SPF: strict (-all), BIMI
Identity Okta, Google Workspace (managed identity)
Cloud Cloudflare (CDN), AWS Route 53 (DNS)
Security Wiz, CAA: 3 issuers restricted
Collaboration Slack, Atlassian (Jira/Confluence)
High-signal related domains
api.contoso.com, login.contoso.com, portal.contoso.com, sso.contoso.com,
admin.contoso.com, status.contoso.com, support.contoso.com
(57 total — 50 more, use --full to see all)
Insights
Federated identity indicators observed (likely Okta — enterprise SSO)
Email security 4/5: DMARC reject, DKIM, SPF strict, BIMI
Email gateway: Proofpoint in front of Exchange
Dual provider: Google + Microsoft coexistence
Examples use Microsoft's fictional company names (Contoso, Northwind Traders, Fabrikam). Tenant IDs, services, and domains are fabricated. No real company is depicted.
Works for Microsoft 365, Google Workspace, or any provider. Also runs as an MCP server for AI agents; the default pip install recon-tool includes MCP support.
| If you need... | Use recon for... | Reach for something heavier when... |
|---|---|---|
| Fast external stack context | Passive DNS, identity-endpoint, CT, SaaS, and posture indicators with no credentials | You need authenticated tenant inventory or asset-management truth |
| Defensive review or vendor diligence | Hedged observations and evidence traces you can verify | You need vulnerability scanning, exploit checks, or host-level facts |
| Automation-friendly output | Stable --json, batch mode, delta mode, and local MCP tools |
You need dashboards, scheduled monitoring, or report generation |
recon is the fast, zero-credential first-pass for external technology-stack and posture visibility. Run it before a vendor diligence call, before a partner integration, before an M&A review, before a hardening audit. Output is hedged, traceable, and shaped for downstream automation.
recon does not replace commercial EASM platforms, active scanners, or continuous monitoring. It is the upstream signal that feeds those tools, with full provenance so you can verify any conclusion before you act on it.
recon starts as a fast passive DNS and certificate-transparency reader. It gathers the public-channel observables: MX records, CNAME chains, SPF and DMARC TXT records, CT log SAN sets, and the unauthenticated identity-discovery endpoints Microsoft and Google publish for tenant resolution.
Those observables are then fed into a small Bayesian network, where exact inference produces 80% credible intervals over high-level claims (M365 tenant, federated identity, email-policy enforcement, CDN fronting, AWS hosting, and so on). The output is an interval, not a binary verdict, and the structural motifs the network surfaces (a CDN in front of an identity provider, an email gateway in front of M365, a secondary GWS deployment alongside primary M365) are the ones single-source detection often misses.
On hardened or heavily-proxied targets, the credible interval widens rather than collapsing on a confident point estimate. This is a construction property of the inference layer: by design, absent evidence is treated as no evidence rather than as evidence of absence. The tool reports what the public channel reveals and is explicit about what it does not.
For the formal model: the missing-data treatment under adversarial assumptions (MNAR via Distributionally Robust Optimization), the calibration principles the credible interval satisfies, and the failure-mode catalog across five hardening postures live in docs/correlation.md.
Requires Python 3.10+.
pip install recon-tool # includes MCP server
pip install -U recon-tool # upgrade
recon doctor # verify connectivityrecon contoso.com # default panel
recon contoso.com --explain # full reasoning + provenance DAG
recon contoso.com --full # everything (services + domains + posture)
recon contoso.com --profile fintech # apply a posture lens
recon contoso.com --confidence-mode strict # drop hedging on dense-evidence targets (v0.11)
recon contoso.com --json # structured JSON for piping
recon batch domains.txt --json # batch (cross-domain token clustering)
recon batch domains.txt --json --include-ecosystem # add v1.8 ecosystem hypergraph
recon contoso.com --chain --depth 2 # follow related-domain breadcrumbs
recon delta contoso.com # diff against last cached snapshot
recon mcp # start MCP server (stdio)Built-in profiles: fintech, healthcare, saas-b2b, high-value-target, public-sector, higher-ed. Custom profiles live in ~/.recon/profiles/*.yaml.
See docs/README.md for the organized documentation index.
recon runs as an MCP server for Claude, Cursor, VS Code, ChatGPT, or any MCP client. The Model Context Protocol lets AI agents call tools like recon directly from your chat.
One-shot install — let recon write the right config block for you:
recon mcp install --client=claude-desktop # or claude-code, cursor, vscode, windsurf, kiro
recon mcp doctor # spawn the server and verify the JSON-RPC handshakeThe install command is idempotent and merge-safe — sibling MCP servers, hand-curated autoApprove lists, custom env vars, and any other keys you've added to the recon block all survive a --force rerun. Use --dry-run first if you want to preview the plan.
Manual install — if you'd rather edit by hand, add this to your client's MCP config:
{
"mcpServers": {
"recon": {
"command": "recon",
"args": ["mcp"],
"autoApprove": []
}
}
}The default install already includes the MCP server. Keep approvals manual until you've decided which tools, if any, you want to trust automatically.
Then ask your AI: "Run a recon lookup on contoso.com and tell me what's running."
See docs/mcp.md for the full tool list, advanced agentic workflows, and per-client config locations.
Claude Code, Kiro, Windsurf, Cursor, VS Code: per-agent install scaffolds live under agents/ — one folder per client with its MCP config and guidance template. Claude Code users get a full plugin (MCP + skill in one install) at agents/claude-code/. The portable AGENTS.md at the repo root is auto-detected by Kiro and other agents.md-aware tools.
Quickest install for AI clients with file-write tools. Paste this prompt to your AI:
Fetch
https://raw.githubusercontent.com/blisspixel/recon/main/agents/claude-code/skills/recon/SKILL.mdand save it to my Claude Code skills directory (~/.claude/skills/recon/SKILL.md) — or to~/.kiro/skills/recon/SKILL.mdif I'm using Kiro. Then runpip install recon-toolandrecon doctorto verify.
The SKILL.md follows the open agentskills.io standard, so the same file works in Claude Code and Kiro.
Stable JSON schema. Downstream consumers can validate recon <domain> --json output against docs/recon-schema.json (raw URL). The schema is the v1.0 stability contract documented in docs/schema.md; drift between schema and emitter is caught by tests/test_json_schema_file.py.
- Coverage depends on public DNS. Organizations behind heavy proxies, with minimal DNS records, or that don't publish SaaS verification tokens will return sparse results. This is fundamental to passive-only collection. When sources transiently fail, the CLI tells you which one and why so you can retry or accept the partial answer.
- Internal workloads are structurally invisible. Server-side API consumption (an org running internal Google Cloud ML, internal AWS data pipelines, internal Snowflake warehouses without public verification tokens, and so on) leaves no trace in public DNS, CT logs, or unauthenticated identity-discovery endpoints. recon cannot tell you what runs internally; it can only tell you what the org publishes externally. The CLI panel calls this out explicitly when it detects a truncated public footprint, so the "Cloud" line surfaces what is observable and acknowledges that the internal stack is a separate question.
- Heuristic, not ground truth. The fingerprint database and signal rules are rule-based and solo-maintained. Confident-looking output can still be wrong. The credible interval is the load-bearing field, not the point estimate: by construction, sparse evidence on hardened targets produces a wide interval rather than a confident-looking point estimate, and the
sparse=trueflag in the JSON output is the operator-facing signal that the layer has hit the passive-observation ceiling. Every detection in the catalog carries a description and a vendor doc URL (v1.9.8+), so a finding can be re-verified against the vendor's own documentation before action. Treat results as indicators for investigation, not as definitive assessments. Don't make business decisions based solely on this output. See docs/correlation.md for the calibration principles the interval satisfies and the failure-mode catalog across hardening postures.
pip install -e ".[dev]" # or: uv sync --extra dev
pytest tests/ # full test suite
ruff check recon_tool/ # lint
pyright recon_tool/ # type check
pre-commit install # activate pre-commit hooksMIT — see LICENSE for details.
This tool queries only public DNS records and unauthenticated endpoints. See docs/legal.md for full disclaimer.