Liquilens MCP Server

LiquiLens MCP — the Failure Radar as agent tools

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

Try it live: liquilens.in/developers · REST catalog: api.liquilens.in/api

LiquiLens is a failure early-warning system for banks and lenders, built on public data with a machine-readable historical-evidence boundary served beside every claim. This MCP 1.8.1 endpoint exposes 23 read-only tools and 5 guided prompts so an agent reads the same status, eligibility flags and cited record a human sees. Its capability inventory is pinned to LiquiLens commit efc4f94b0f4180ae0847bed96d32805d195d368e.

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

For Hermes and OpenClaw, use the native setup guides:

• Hermes: configuration, connection checks and a first research task
• OpenClaw: configuration, discovery checks and a first research task

The current guides select thirteen research tools across LiquiLens, Seiche and Undertow, including GIFT City, reference FX and gold scenarios. Their dated native-client receipts retain the original nine-tool selection and exact verification scope. Public research requires no account or API key; fair-use limits apply and your model provider may charge separately.

The free starter kit also includes a Python brief, a manual n8n funding workflow and configurations for the clients below.

OpenClaw users can also install the optional research instructions from ClawHub:

openclaw skills install @beepboop2025/liquilens-trading-research --version 1.1.0

Connect the MCP servers using the OpenClaw guide first. The skill supplies task instructions and a configuration template; installation does not configure servers or run research. Its source bundle is MIT-0; that license does not relicense source data or API responses.

Checked on 4 October 2026 (UTC): version 1.1.0 is public and ClawHub's version-pinned verification result reports a passed security check with a benign, high-confidence verdict. Its embedded detailed scanner report retains seven findings, including medium external-transmission flags for the public MCP URLs, and reports partial analysis. Review the versioned security audit before installation and use public research inputs. Its isolated Linux install recorded the correct version and matched all three reviewed source files. ClawHub's public verifier and the installed Linux client's version-pinned openclaw skills verify now both report pass, with the generated Skill Card present. The upload is unsigned and has no server-resolved GitHub import provenance. A fresh isolated macOS install after storage recovery also matched all three source-file hashes, and its version-pinned verification reports pass. The earlier macOS timeout remains a separate failed attempt. No model or research tool ran in these checks. The direct MCP configuration is available independently of the ClawHub skill.

Claude Code:

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

Codex:

codex mcp add liquilens --url https://api.liquilens.in/mcp

Cursor: merge the remote MCP config into .cursor/mcp.json for one project or ~/.cursor/mcp.json for all projects. The download includes the companion Seiche funding endpoint. Preserve existing entries. For an existing Codex config, use the TOML snippet and merge its server entries into ~/.codex/config.toml.

Claude.ai / ChatGPT: add a custom connector or MCP app with the endpoint above where that feature is available in your workspace.

This repository is the public discovery and documentation mirror. The hosted implementation is maintained in a private core repository. This public mirror pins its inspectable capability contract to the reviewed source revision above. The official Registry entry uses the name io.github.beepboop2025/liquilens. This repository's Registry metadata revision 1.8.1 points to the public mirror and free starter kit. The hosted MCP now reports version 1.8.1 at the same endpoint. The Registry metadata and runtime happen to share a version number; they remain separately verified contracts. The pinned source and tool inventory above describe the current runtime. The existing Registry record continues to identify this public mirror.

The current runtime separates fresh model results from reviewed filing facts: expired filing scores are excluded from the live board, current disclosures retain their official source and publication clocks, and inactive institutions remain identifiable in historical evidence. Its US evidence includes the June 2026 panel and reviewed 2026 failure notices through 25 September.

Query source histories

The separate source-data MCP and Python client expose funding histories, bank filings and Bitcoin/Liquid settlement observations with source receipts, native units and capture clocks. Use the live coverage table or import its OpenAPI contract.

Run a research task

The research recipes run with Python 3.11+ and its standard library. No API key, LLM or Python package install is needed. Download and inspect financial_research.py, then run:

python3 financial_research.py bank-review
python3 financial_research.py bank-review --slug cosmos-ucb
python3 financial_research.py funding-brief

The first command discovers covered bank slugs. The second checks coverage before retrieving that exact bank's sourced asset-quality history; an absent slug stays not_covered. The funding brief calls Seiche's money-market desk. Returned evidence retains its native source dates, eligibility, unavailable states and limitations; a successful request does not imply fresh or complete evidence. Each run is bounded and performs no scheduling or trading. Operators should add --verification so their checks are labelled synthetic and excluded from adoption totals.

For a visual workflow, download the n8n bank-review workflow and follow the import guide. It uses a manual trigger and no LLM. The guide records its execution-verification status; a downloadable workflow does not imply acceptance into n8n's template library.

Protocol compatibility

• 2026-07-28: stateless requests use server/discover, per-request _meta, MCP-Protocol-Version, and mirrored Mcp-Method / Mcp-Name routing headers.
• 2025-11-25, 2025-06-18, and 2025-03-26: retained legacy initialization, tools, prompts, notifications, batching, and ping behavior.
• Tools, prompts, and discovery responses are deterministic. Modern list responses carry public cache metadata.
• resources/list and resources/templates/list are supported and currently return empty catalogs. resources/read returns an explicit not-found error instead of inventing a resource.

Tools

Prompts

The governance line

The generative layer explains; deterministic screening policy scores. Historical diagnostics retain their served evidence status and eligibility flags and do not become validated-backtest or real-money evidence merely because a deterministic engine produced them. Screens, not ratings; not investment advice. Institutions without a vetted dossier are absent by design — the tools say so rather than inventing a score.

The three historical status tokens are part of the API contract, not marketing copy: PERIOD_END_PROXY_CONSTRUCTION_PIT for India, CURRENT_AMENDED_CONSTRUCTION_PIT for the United States, and NAMED_CASE_FILES_CONSTRUCTION_PIT for Europe. In the current release, every market serves validated_backtest_eligible: false and real_money_eligible: false.

Siblings from the same lab

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

Product: liquilens.in · Live demo: demo.liquilens.in

Quant research agent integrations

Native framework tools and cited quant pipeline captures connect Seiche funding, LiquiLens bank diagnostics and Undertow market liquidity through compact read-only tables. LangChain/LangGraph, CrewAI, OpenAI Agents and Pydantic AI share one evidence contract and repeat-call revision tokens. Current published history is not an as-published vintage archive. No execution authority or institutional-adoption claim is implied.