Pacevera MCP server

Pacevera Fitness Decision Engine

Pacevera is a deterministic fitness decision engine exposed through the Model Context Protocol (MCP). It receives normalized training and health evidence supplied by the calling AI host, evaluates recovery and training constraints, and returns a structured decision.

The primary operation is to evaluate an existing scheduled session. A decision describes the change from the scheduled session to the resulting session:

scheduled session (from) -> resulting session (to)

Each decision includes the relevant evidence, applied rules, confidence, signal coverage, and limitations.

Scope

Pacevera provides the following capabilities:

• Deterministic calculation of recovery state, readiness, muscle-group fatigue, and training load.
• Session decisions with the types keep, adjust, substitute, defer, and advance.
• Constraint handling for injuries, equipment, available time, and exercise substitutions.
• Training-plan generation and caller-owned plan adjustment through preview and commit operations.
• Structured decision provenance through rule identifiers, measured values, source information, and version metadata.
• Normalization support for Apple Health, Google Health Takeout, Garmin, Strava, Oura, and WHOOP data shapes.

Pacevera does not connect to these providers on behalf of the user. The calling host must obtain and normalize provider exports or API responses before passing them to an MCP tool.

Architecture

AI host
  └─ Collects the user's question and evidence
       └─ MCP server / desktop extension
            ├─ Evidence normalization
            ├─ Fitness-state calculation
            ├─ Training-load calculation
            ├─ Deterministic rule evaluation
            ├─ Decision and plan operations
            └─ Structured result: Decision / Action / Reason

The AI host is responsible for interpreting the user's request, collecting evidence, selecting tools, and presenting the result. Pacevera performs the calculations and rule evaluation; it does not call a model to generate a decision. Given the same code, rule set, and input, the result is deterministic.

Decision and recommendation

decidesession requires scheduledSession to establish the prior state. If no scheduled session is provided, the tool returns noscheduledsession rather than creating a session. Use generateplan when a plan is required.

Decision output follows five layers:

Evidence -> Fitness State -> Decision -> Action -> Reason

Decision intent and resulting action are separate fields. The same intent may produce different actions depending on the user's equipment, available time, injuries, and scheduled session.

MCP tools

Decision and planning tools

Supporting and read tools

Shortened. The full README is on GitHub.