Lingshu Solver

Lingshu Solver

Category: Certified Real-Root Computation

A deterministic solver for systems of real equations. The same problem always produces the same

answer — no language model, no randomness, no hallucination. Every solution it reports is

reproducible and checkable by substitution, and the result carries structured decision fields that

tell the calling agent exactly what to do next.

Built for AI agents. Connect it as an MCP tool and your model stops guessing arithmetic. It is a deterministic, non-LLM numerical engine: same input, same answer, every reported solution checkable by substitution, and — crucially — every result comes with a trust block that tells the agent whether the answer is complete, safe to use, or needs more work. You branch on those fields; you never re-derive completeness yourself. Three things matter to a calling agent, and all three are built in:

It also does 1D/2D/3D geometry through one geometry tool (130 closed-form ops: distance, intersection, area, volume, angle, convex hull, point-in-polygon, rotation, bounding box, plus the classical triangle and circle theorems — five centres, Euler line and OI² = R(R−2r), Heron, Stewart, Ceva, Menelaus, Ptolemy, Miquel, Napoleon, Pick, pole/polar, Brahmagupta, plus a tropical/convex bridge (Newton polytope, BKK mixed-volume bound, regular-subdivision multiplicities) — and 3D tetrahedron/Monge point). Same contract as the solver: a trust block tells the agent whether the value is exact, whether the computation *proved* there is definitely_none, or whether the input itself is degenerate (parallel / collinear / coplanar) — and in the last case the correct move is to report the degeneracy, never to invent a number.

Humans can use the web page too (zero install) — but the design target is the agent: compact tool descriptions, structured errors that say what to change, and no prose the model has to pay for on every turn.

🔬 Live demo against the production endpoint: https://hclj-1409755229.cos.ap-guangzhou.myqcloud.com/lingshu-solver/demo.html — call the real MCP endpoint right from the browser and watch poly_roots return certified real roots and verify judge a candidate value. Thirty seconds is enough to see why "an LLM will mis-compute this, Lingshu can certify it".

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30 seconds to start

① AI agent (MCP — pick either form)

// local stdio — permanently free, unlimited, offline. Recommended.

{ "mcpServers": { "lingshu-solver": { "command": "npx", "args": ["-y", "lingshu-solver"] } } }

// hosted endpoint — no install, always on, reachable over the public internet

{ "mcpServers": { "lingshu-solver": { "type": "http", "url": "https://hongchenlingjing.com/mcp" } } }

It also works if you never pay: the hosted endpoint runs on an honor system — pass

"honorPaid": true in the solve arguments and the call is released for free (no verification, no

balance deduction). The npx local version and the web version are permanently free. If you do want to

support it, see the payment page — a corporate account, self-service

crediting, effective immediately, no human approval anywhere in the loop.

② Web (zero install, permanently free)

• Main site: https://hongchenlingjing.com/
• Mirrors: https://genesis-plan.github.io/lingshu-solver/ · https://hclj-1409755229.cos.ap-guangzhou.myqcloud.com/lingshu-solver/index.html

Type equations into the box (for example x^2 + y^2 = 25 and x + y = 7) and press solve. Everything is computed inside your browser; the equations never leave your device.

③ Developer

git clone https://github.com/genesis-plan/lingshu-solver.git

cd lingshu-solver node mcp-server.js # start the local MCP (stdio) server node test/regression.js # standing regression suite

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Discovery — where agents find Lingshu

• npm — npx -y lingshu-solver (local stdio, permanently free, offline)
• Hosted endpoint — https://hongchenlingjing.com/mcp (MCP Streamable HTTP; always on, no install, no key)
• Auto-indexed MCP directories — Glama · HiMCP · LobeHub

To surface it in more clients, point any MCP client at the hosted endpoint above, or submit the repo to Smithery, the official MCP Registry, or mcp.so. The hosted endpoint is the canonical discovery URL.

Honest boundaries

Not guaranteed: 100% exhaustiveness for every input, or convergence inside budget for highly pathological systems. That is the honest floor of numerical mathematics ("guaranteeing all solutions" is undecidable in the general case), not a defect to be fixed.

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Documentation

The machine-readable description for AI agents lives at llms.txt.

Three of the design docs are in English; the deep-dive docs (design rationale, technical reference, licence and pricing, contract, versioning, history) are still in their original Chinese. Each row below tells you which is which.

If you read only one page, read 01 · Product role — it states the limits and the approved wording.

Privacy and security commitments are published separately: privacy.html.

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Licence (summary)

Free for non-commercial use; commercial use requires written permission (a proprietary licence of our own, not an open-source licence):

• Non-commercial, free: personal study / research / teaching / evaluation; internal use by non-profit organisations and educational institutions; internal evaluation by teams with annual revenue up to RMB 1 million (≤ 3 instances).
• Commercial use needs prior written permission: any product, service or business operated for profit, resale of SaaS/cloud/API, integration,