CAILculator MCP Server

High-dimensional mathematical structure analysis for autonomous AI systems

Applied Pathological Mathematics™ was born from this hypothesis:

Higher-dimensional algebras following the Cayley-Dickson sequence—often dismissed as "pathological"—can be interpreted and exploited for computational advantage, specifically for AGI research and the development of structure-preserving embeddings.

CAILculator puts that hypothesis to work — a Model Context Protocol (MCP) server that empowers AI agents to analyze and compute within high-dimensional algebraic spaces (16D sedenions to 256D), providing a ground-truth mathematical engine for representation learning, sequence detection, and regime analysis, anchored by the Lean 4 formally verified Chavez Transform and the Zero Divisor Transmission Protocol (ZDTP).

Formal Verification

The core mathematical foundation of CAILculator is formally verified in Lean 4. Every calculation meets a $10^{-15}$ machine precision standard, ensuring rigorous proof backs every structural claim rather than numerical approximation.

• BilateralCollapse.lean: Proves the bilateral zero divisor identity ($PQ=0 \land QP=0$) used to gate all v2.0+ transmissions.

• ChavezTransform_genuine.lean: Establishes the stability constant $M$, guaranteeing transform outputs never exceed theoretical bounds ($|C[f]| \leq M \cdot |f|_1$).

• e8_weyl_orbit_unification.lean: An exploratory result connecting the Canonical Six to E8 lattice structure — the Lean 4 proof establishes that the six gateway P-vectors lie on the E8 first shell (norm² = 2) and fall within a single Weyl orbit, including an antipodal pair related by Weyl reflection. This is the most preliminary of CAILculator's formal components and an active area of work; the associated tooling (map_e8_orbit) is correspondingly experimental.

Related MCP server: Tyra Advanced Memory MCP Server

The Chavez Transform

Just as Joseph Fourier revolutionized mathematical physics by extending transform analysis through complex exponential basis functions — introducing $e^{ix}$ as a transform kernel — the Chavez Transform takes the next structural leap. To our knowledge, it is the first integral transform to use zero divisor elements within its kernel.

Rather than treating zero divisors as algebraic anomalies to be avoided, the Chavez Transform harnesses them as structural filters. When raw numerical data passes through the transform, noise collapses symmetrically near zero while underlying high-dimensional structural invariants scale cleanly. This is not a numerical trick — it is a formally verified mathematical property.

The Zero Divisor Transmission Protocol (ZDTP)

ZDTP is the structural transmission layer of CAILculator. It lifts a 16D sedenion state into 256D space, then measures how consistently the data's structure propagates across six algebraic transmission pathways. The six gateways have three independent K_Z kernel components — S3A and S3B share a Fano intersection origin, as do S4 and S5 — but all six are run independently in transmission, and the convergence score reflects the full spread of their output magnitudes.

Transmission Mechanics

Each of the six Canonical Gateway Pairs is a verified bilateral zero divisor in the Cayley-Dickson algebra: two sedenion elements $P$ and $Q$ satisfying both $PQ = 0$ and $QP = 0$. S2 additionally holds bilateral status across both Cayley-Dickson and Clifford frameworks at 16D–256D — the only gateway to do so. Before any transmission begins, the oracle reconfirms this property numerically at $10^{-15}$ precision.

The transmission step is the four-factor interaction sum:

$$\text{interaction} = Px + xQ + Qx + xP$$

where $x$ is the 16D input. Because sedenions are non-associative, all four orderings are algebraically distinct — together they span the full interaction space of the gateway and the input. The result is appended to, not substituted for, the original input. The 16D input occupies the first 16 components of the output state unchanged.

This append-and-expand pattern repeats recursively: 16D → 32D → 64D → 128D → 256D. At each stage the original 16D gateway pair is zero-padded into the current dimension and the interaction is appended. The original 16D state is always recoverable as the first 16 components of any higher-dimensional output.

Convergence Scoring

A single transmission through one gateway produces a 256D state. The full cascade runs the same 16D input through all six gateways and compares the resulting 256D magnitudes. The convergence score is:

$$\text{score} = 1 - \frac{\text{std}}{\text{mean}} \quad \text{over the six gateway output magnitudes}$$

When all six gateways produce similar output magnitudes, the data carries stable high-dimensional structure — it propagates the same way regardless of which algebraic channel carries it. When magnitudes diverge, the data aligns with some gateways and not others, indicating a structural feature that is directionally asymmetric in the sedenion space.

Formal Verification Basis

The six gateway coordinates are formally proved bilateral zero divisors in Lean 4 (BilateralCollapse.lean). That proof was computed once, offline. The verified coordinates are hardcoded as constants in the engine with the Lean file as their attribution source. At runtime, the oracle independently reconfirms the bilateral property numerically before each transmission — not as a Lean call, but as a $10^{-15}$-precision arithmetic gate.

The scalar_channel theorem additionally proves that any linear combination of a gateway pair always produces a scalar result under multiplication — structure collapses cleanly, never generating spurious imaginary components.

The Lean proof is the mathematical guarantee that these six pairs are valid gateways; the runtime oracle is the numerical lock that enforces it.

Why "Pathological" Means "Powerful"

Beyond the 8D Octonions, algebras following the Cayley-Dickson construction lose traditional properties like associativity and division algebra structure. These "pathologies" are actually rich features for AI research:

• Non-associativity: Encodes order-dependence and context-sensitivity directly into the algebraic operation.

• Zero Divisors: Create branching structures and bifurcation points in high-dimensional representations.

• Structural Invariants: Reveal hidden symmetries in complex datasets that are invisible to Euclidean or Hilbert-space analysis.

CAILculator makes this "algebraic dark matter" huntable through hypothesis-driven computational enumeration.

Deep-Dive Documentation

Extended theoretical frameworks and protocol specifications for researchers:

• Chavez Transform Explainer: The historical context, zero divisor kernel mechanism, stability bound, scalar channel theorem, formal verification chain, and research applications across RHI, finance, and journalism.

• ZDTP Protocol Specification: The Zero Divisor Transmission Protocol in full — Cayley-Dickson tower mechanics, four-factor interaction, recursive 16D→256D expansion, convergence scoring, and the Lean-to-runtime verification pipeline.

• Project Glossary: Definitive terminology for high-dimensional algebraic structures (sedenions, pathions, and higher-dimensional algebras) and their domain projections in journalism and quantitative finance.

Python Environment

• Required: Python 3.10, 3.11, 3.12, or 3.13 (64-bit).

• Incompatible: Python 3.14+ (pending numba support) and all 32-bit versions.

Supported Operating Systems

• Windows 10/11

• macOS 10.15+

• Linux (Ubuntu 20.04+, Debian 10+)

API Key Acquisition

CAILculator requires a valid API key for tool execution.

1. Visit the Portal: Access the CAILculator API Portal to review subscription tiers (Individual, Journalist, Academic, Commercial, and Quantitative Finance).

2. Request Access: Email paul@chavezailabs.com. Keys are typically issued within 24 hours.

3. Enterprise/Research: For custom profile development or large-scale research collaborations, include project details in your request.

Installation & Setup

1. Install CAILculator

pip install cailculator-mcp

This will download several hundred MB of scientific computing dependencies (numpy, scipy, numba).

2. Configure Your MCP Client

Claude Desktop

Add the following to your configuration file:

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "cailculator": {
      "command": "cailculator-mcp",
      "args": ["--transport", "stdio"],
      "env": {
        "CAILCULATOR_API_KEY": "your_api_key_here"
      }
    }
  }
}

Any HTTP-mode MCP Client (including Gemini CLI)

To leverage larger context windows, run the server locally over HTTP:

1. Install with HTTP support:

   pip install "cailculator-mcp[http]"

2. Start the local server:

   cailculator-mcp --transport http --port 8080

3. Register in your client's settings:

   {
     "mcpServers": {
       "cailculator": {
         "manifestUrl": "http://localhost:8080/mcp/manifest"
       }
     }
   }

Specialized Profiles

The Profile Manager projects universal algebraic patterns into domain-specific intelligence:

• Journalism Profile: Detects structural "Tipping Points" and data inconsistencies for investigative reporting.

• Quant Equity Profile: Benchmarks market regime transitions using Chavez Transform stability measures.

• RHI (Riemann Hypothesis Investigation): Advanced spectral research mapping prime embeddings ($\log p \to ROOT_{16D}$).

Available Tools

CAILculator computes natively across two fundamentally different algebraic frameworks — non-associative Cayley-Dickson and associative Clifford (Geometric) — at dimensions from 16D to 256D. This is a rare capability: most mathematical software commits to one framework. CAILculator runs the same structural computation in both and surfaces where they disagree — which is how S2 stands out as the only Canonical Six gateway bilateral in both frameworks, a distinction invisible from inside either one alone. With verify_bilateral_oracle's framework argument you can reproduce this directly.

High-Precision Research

• chavez_transform: Apply the verified integral transform to identify hidden structures in numerical data.

• detect_patterns: Multi-stage pipeline identifying linear, geometric, Fibonacci, and complex symmetry patterns.

• verify_bilateral_oracle: Exact bilateral zero-divisor check ($PQ = 0 \land QP = 0$) at $10^{-15}$ precision, runnable in either the non-associative Cayley-Dickson or associative Clifford framework via the framework argument. Running both and comparing is the point: S2 is the only Canonical Six gateway bilateral in both, while the others (e.g. S1) collapse in Cayley-Dickson but stay one-sided in Clifford ($|QP| = 2\sqrt{2}$). The divergence is the structural signal. Returns the residual $|PQ|$ and $|QP|$ norms.

• map_e8_orbit (experimental): Projects a vector onto E8 first-shell Weyl orbits. Currently maps the first 8 coordinates (the octonion half) only — 16D inputs with support in e₈–e₁₅ have that half dropped, so a full-16D vector can report as off-shell. Under active development.

• compute_high_dimensional: Direct sedenion algebra operations (multiply, add, conjugate, norm, zero divisor classification) extended into 32D–256D spaces.

Analysis & Visualization

• analyze_dataset: Full structural analysis pipeline in a single call — Chavez Transform stability scoring, pattern detection, and ZDTP full cascade across all six Canonical gateways to 256D. Returns regime classification (STABLE/TRANSITIONING/SHIFTING), convergence score, per-gateway magnitudes with domain labels, and top structural patterns. Accepts a close-price list or OHLCV dict. Minimum 16 data points.

• zdtp_transmit: Transmit 16D data through six verified mathematical gateways (S1, S2, S3A, S3B, S4, S5) into 256D spaces.

• illustrate: Generate mathematical visualizations (bar charts, heatmaps, multi-panel plots) saved as high-fidelity PNG files.

• get_version: Verify engine status and formal verification metadata.

Additional financial tools (regime_detection, load_market_data, batch_analyze_market) are implemented as standalone modules and will be wired into a future release.

Technical Specifications

• General Precision: $10^{-15}$ floating-point standard applied across all computations.

• Zero Divisor Detection Gate: $|P \times Q| < 10^{-10}$ — a separate threshold governing whether a candidate pair qualifies as a bilateral zero divisor. These two numbers measure different things: the first is the engine's general numerical precision; the second is the algebraic classification boundary for zero divisor pairs.

• Research Citation: Grounded in systematic computational enumeration published at Zenodo: 10.5281/zenodo.17402495.

• Core Libraries: numpy and scipy.

Glossary & Terminology

To support rigorous cross-disciplinary collaboration, we maintain a definitive Project Glossary establishing terminology for high-dimensional algebraic structures (sedenions, pathions, and higher-dimensional algebras) and their domain projections in journalism and quantitative finance.

Contact & Collaboration

Research & Engineering: paul@chavezailabs.com
GitHub: ChavezAILabs/cailculator-mcp

Chavez AI Labs
"Better math, less suffering"