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MCP server for LiquiLens, the failure radar for banks and lenders: institution failure risk, crisis replay evidence and grounded answers for AI agents at api.liquilens.in/mcp

MCP ServersOfficial Registry0 stars0 forksUpdated today
Install in Claude Code / Claude Desktop
Method: Manual
Claude Code CLI
git clone https://github.com/beepboop2025/liquilens-mcp
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "liquilens-mcp": {
      "command": "node",
      "args": ["/path/to/liquilens-mcp/dist/index.js"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Clone https://github.com/beepboop2025/liquilens-mcp and follow its README for install instructions.
Use cases

MCP Servers overview

# LiquiLens MCP — the Failure Radar as agent tools

**Endpoint:** `https://api.liquilens.in/mcp` (streamable HTTP, no auth, no install)

LiquiLens is a failure early-warning system for banks and lenders, built on public
data with the validation record served beside every claim. This MCP server lets any
LLM agent read the same record a human sees.

It now reads both ends of the chain: which institutions are fragile, and whether that
stress is actually crossing into the real economy: companies rolling paper and drawing
credit lines, households falling behind. Channels that cannot be read are named in
`cannot_see` rather than reported as calm.

## Add it

Claude Code:

    claude mcp add --transport http liquilens https://api.liquilens.in/mcp

Claude.ai / ChatGPT / Cursor: add a custom connector or MCP server with the URL above.

## Tools

| Tool | What it serves |
|---|---|
| `failure_radar_board` | The live board: every Indian lender with a fresh vetted dossier — failure PD term structure (12/24/36m), RBI action-zone status, funding fragility, market distance to default, watchlist tier under a published rule |
| `failure_radar_institution` | One institution in depth: PD trajectory with drivers, PCA/SAF headroom history, forensic screen, market reading |
| `evidence_markets` | The validation record per market, one headline each |
| `evidence_india` | The Indian record: 48 institutions across two decades, misses and false alarms included |
| `evidence_institution` | One Indian institution's full crisis replay with sourced quarterly rows |
| `evidence_us` | The US record: 552 FDIC failures since 2008, 72.8% recall at 21.7-month median lead, marquee replays including the honest fraud miss |
| `evidence_europe` | Seven audited European case files, Northern Rock to Credit Suisse, replayed through unrecalibrated lenses |
| `universe_search` | RBI's official registered-NBFC registry (9,000+ entries) |
| `rbi_supervisory_tape` | Latest RBI enforcement actions, each linking to the RBI's own page |
| `verify_published_record` | Independent cryptographic verification of the as-published record |
| `corporate_transmission_board` | Is funding stress reaching nonfinancial firms? The commercial-paper market, bank credit lines, real-economy confirmation (claims, capex, inventories, openings, business bankruptcies) and a balance-sheet context channel, with a TRANSMITTING/CONTAINED verdict |
| `household_credit_board` | Is stress transmitting through household balance sheets? Fed delinquency and charge-off legs against each leg's own trailing decade, two-sided revolving-credit velocity, debt service as unscored context |

## The governance line

The generative layer explains; **only the validated deterministic layer scores.**
Numbers come from deterministic engines with model cards, never from a model.
Screens, not ratings; not investment advice. Institutions without a vetted dossier
are absent by design — the tools say so rather than inventing a score.

## Siblings from the same lab

- [Seiche](https://api.seiche.info/mcp) — US money-market funding stress (the plumbing)
- [groundcheck](https://groundcheck.seiche.info) — claim grounding and citation verification
- Palimpsest (`https://api.seiche.info/palimpsest/mcp`) — live internet-censorship signals
- [Undertow](https://liquilens.in/undertow/) — the cross market liquidity map: daily tiered board, exit cost at position size, Telegram front door at [t.me/undertow_LiquiLens_bot](https://t.me/undertow_LiquiLens_bot)

Product: [liquilens.in](https://liquilens.in) · Live demo: [demo.liquilens.in](https://demo.liquilens.in)

What people ask about liquilens-mcp

What is beepboop2025/liquilens-mcp?

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beepboop2025/liquilens-mcp is mcp servers for the Claude AI ecosystem. MCP server for LiquiLens, the failure radar for banks and lenders: institution failure risk, crisis replay evidence and grounded answers for AI agents at api.liquilens.in/mcp It has 0 GitHub stars and was last updated today.

How do I install liquilens-mcp?

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You can install liquilens-mcp by cloning the repository (https://github.com/beepboop2025/liquilens-mcp) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is beepboop2025/liquilens-mcp safe to use?

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beepboop2025/liquilens-mcp has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.

Who maintains beepboop2025/liquilens-mcp?

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beepboop2025/liquilens-mcp is maintained by beepboop2025. The last recorded GitHub activity is from today, with 0 open issues.

Are there alternatives to liquilens-mcp?

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Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.

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