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lingshu-solver
Advanced tools
Lingshu Solver — deterministic (non-LLM) MCP tool for solving systems of real equations, up to 6 variables: interval arithmetic + affine arithmetic + constructive Krawczyk certification, zero dependencies, offline, no data upload, unproven-completeness ho
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:
| What an agent needs | What this tool does |
|---|---|
| A verdict it can branch on without guessing | every result carries a conclusion — exactly one of 全部解 / 部分解 / 无解 / 计算资源不足 (all / partial / none / budget-exhausted) — plus canAssert for programmatic use, so you never have to re-derive completeness yourself |
An 8-level trust block for the "how much do I believe it" question | trustLevel (8 values, incl. complete_but_shown_partially — search finished, only the page was capped), safeToUse, and provenCount / candidateCount so you never have to count solutions yourself |
| Honesty about "no answer" | an empty result is never silently "no real solution" — meaningOfEmpty distinguishes proven empty / not found within budget / input not solvable |
| A proof of completeness when one exists | trust.completeness compares the number of solutions found against a theorem-proven upper bound (BKK mixed volume of the Newton polytopes, Bézout, multi-homogeneous Bézout, Milnor–Thom, Descartes/fewnomial). complete = the count hits the bound, so you may say "these are all of them". Anything else says so instead of guessing |
| Inequality constraints that are actually enforced | x^2=0, x>0 returns no solution, not x=0 — a final gate substitutes every candidate back into the problem's own inequalities (open endpoints included) and drops the violators |
| A way to fix its own mistake | verify returns the certified corrected value and how far off the original was, so a wrong number becomes a repair, not a retry loop |
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".
| Yes | a deterministic (non-LLM) numerical engine for systems of real equations; algebraic equations and common transcendentals (sin/cos/tan/log/exp/sqrt/abs) all work |
| No | a symbolic CAS (no analytic derivation), an ODE solver, an integer-programming solver, and it does not claim completeness it did not prove — the completeness block is a theorem-backed bound where one exists, and unknown everywhere else |
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": truein thesolvearguments and the call is released for free (no verification, no balance deduction). Thenpxlocal 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)
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
| Dimension | What it means |
|---|---|
| Verified solutions | every reported solution is Krawczyk-certified (tier=proven); error is within the certified radius; mathematically faithful |
| Completeness | proved where a bound exists. For polynomial systems the engine derives a rigorous upper bound on the number of isolated solutions (BKK mixed volume of the Newton polytopes, Bézout, multi-homogeneous Bézout, Milnor–Thom, Descartes/fewnomial) and reports trust.completeness. complete means the number found equals that bound, so "these are all the solutions" is a theorem, not a guess. incomplete / unknown never pretends otherwise |
scope caveat | the bound's scope is a closed enum. "(C*)^n" (BKK) counts only solutions where every variable is non-zero, so a solution with a zero coordinate is outside the count — complete there means "all non-zero solutions found" |
truncated semantics | only means "the global branch search did not finish inside the budget"; it does not mean solutions were missed. In most cases every real solution was found |
| Variables | ≤ 6 |
| Equations | 1–64 (server-side guard), and the count must be ≥ the variable count |
| Search range | default ±1e6 per variable; supply domain explicitly for fast-growing functions (exp/sinh) |
| Output precision | No user-facing switch. Internal computation is fixed at 6 decimals; the agent-facing display is 4 decimals (precisionDecimals) while solutions[].values keep full float precision |
| Determinism | no random branching; identical input always yields identical output, so caching is safe |
| Data | web version sends nothing upward; local version runs offline; the hosted endpoint does not persist equation contents |
| Dependencies | zero third-party dependencies (Node built-ins and standard browser APIs only) |
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.
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.
| Doc | Language | Contents |
|---|---|---|
| 01 · Product role | EN | what it is, what problem it solves, who it is for, capabilities and limits, official wording |
| 02 · User guide | EN | the three forms, the MCP tool contract (input/output/errors), self-hosting, FAQ |
| 05 · Use cases | EN | applicable scenarios (agent backend / multi-agent / off-chain computation / tax and finance control / on-prem / education / audit) and the ones that do not apply |
| 03 · Design ideas | 中文 | six design principles, why it is trustworthy, why no LLM, what it deliberately refuses to do |
| 04 · Technical reference | 中文 | mathematical framework, algorithm pipeline, operator table, hard spec constraints, test suite |
| 06 · Commercial licence and pricing | 中文 | licence model, what is free, hosted-endpoint metering, enterprise annual licence, invoicing |
| 07 · Licence contract | 中文 | commercial licence template, clause walkthrough, signing flow |
| 08 · Versioning | 中文 | version semantics, release flow and the consistency checklist, compatibility promises, history |
| 09 · Project history | 中文 | how it got from the original problem to where it stands now |
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.
Free for non-commercial use; commercial use requires written permission (a proprietary licence of our own, not an open-source licence):
1.0.4. Versions 1.0.3 and earlier remain under
the Apache License 2.0 as published at the time (a historical fact, not revocable, and it does not extend
to later versions).Full terms in LICENSE · scope and pricing in 06 · Commercial licence and pricing · contract in 07 · Licence contract
FAQs
Lingshu Solver — deterministic (non-LLM) MCP tool for solving systems of real equations, up to 6 variables: interval arithmetic + affine arithmetic + constructive Krawczyk certification, zero dependencies, offline, no data upload, unproven-completeness ho
We found that lingshu-solver demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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