In China, brands like McDonald's provide official MCP server interfaces. They give comprehensive, lab-tested nutritional inspection data (exact calories, macronutrients, sodium, micronutrients, etc.).

How this would work in Bevel:

Why this is valuable:

I’d like to request that Bevel Intelligence gain the ability to connect directly to external fitness platforms and digital gym equipment (eg: Speediance, Aeke Vitruvian and others) through MCP (Model Context Protocol) and/or APIs.

Rather than requiring ChatGPT, Claude, or another AI assistant to sit between Bevel and connected fitness equipment, I would love to see Bevel Intelligence itself become the AI orchestration layer.

The concept would be:

Bevel Health & Recovery Data ↔ Bevel Intelligence ↔ Digital Gym / Fitness Platforms

Why this would be powerful

Bevel already understands a large part of the user’s overall health and fitness picture, including things such as:

* Recovery/readiness

* HRV and resting heart rate

* Sleep

* Strain and training load

* Activity

* Nutrition and macros

* Weight and body-composition trends

* Workout history

* User goals

Digital gyms and connected strength-training equipment have another extremely valuable dataset: what actually happens during resistance training.

Depending on the equipment, this can include:

* Exercises performed

* Resistance used

* Sets and reps

* Training volume

* Exercise-specific performance

* Strength progression

* Estimated 1RM

* Programmed versus completed workouts

* Other equipment-specific performance metrics

If a digital gym exposes this information through an MCP server or API, Bevel Intelligence could connect directly to it and combine that information with Bevel’s existing health and recovery data.

Bevel Intelligence could become the training decision-maker

For example, a user could wake up and ask:

“What should I do for my workout today?”

Bevel Intelligence could evaluate:

* Recovery

* Sleep

* HRV compared with baseline

* Recent strain

* Nutrition

* Recent strength-training volume

* Muscle groups recently trained

* Exercise-specific performance

* Strength progression

* The user’s current goals

It could then determine whether the planned workout is appropriate.

If recovery is excellent and performance has been improving, Bevel might recommend progressing resistance or volume.

If recovery is poor, sleep was inadequate, and recent training volume was high, Bevel might recommend reducing intensity or volume, changing exercises, or moving the workout.

With permission, Bevel could send the workout directly to the gym

This becomes even more useful if Bevel Intelligence is allowed to use write actions exposed by the digital gym’s MCP/API.

After recommending a workout, Bevel could ask:

“Would you like me to adjust today’s workout?”

If the user approves, Bevel Intelligence could create or modify the workout directly on compatible equipment.

The user could then simply walk up to the machine and begin training.

Afterward, Bevel Intelligence could retrieve the completed workout—including actual sets, reps, resistance, volume and performance—and use those results when making future recommendations.

That creates a continuous feedback loop:

Recovery & health → Bevel Intelligence → personalized workout → digital gym → actual performance → Bevel Intelligence → future adaptation

Progressive overload could become truly adaptive

This could also make Bevel’s training recommendations substantially more individualized.

Instead of relying primarily on scheduled progression, Bevel Intelligence could see how the user actually performed.

For example:

“You completed all three sets of this exercise above the target rep range during your last two workouts, your recovery is good today, and your recent performance trend is improving. I recommend increasing resistance.”

Or:

“Performance on this movement has declined for three sessions while recovery and sleep have also been below baseline. I recommend maintaining the current resistance and reducing today’s volume.”

That connects recovery directly to actual strength performance.

Nutrition could become part of the same feedback loop

This would also make Bevel’s nutrition capabilities more powerful.

A user could tell Bevel Intelligence:

“I want to lose weight while maintaining or increasing my strength.”

Bevel could monitor:

* Calories

* Protein and other macros

* Weight trend

* Recovery

* Sleep

* Activity

* Training load

* Actual strength performance from the connected gym

If weight is decreasing while strength and recovery remain stable or improve, Bevel can recognize that the current approach is working.

If weight is decreasing but strength, workout performance and recovery are simultaneously deteriorating, Bevel could recognize that relationship and recommend changes to nutrition, recovery or training.

MCP could avoid building individual gym integrations

The biggest advantage of supporting MCP connections inside Bevel Intelligence is that Bevel would not necessarily have to develop and maintain a custom integration for every digital gym or fitness platform.

Equipment manufacturers and third-party developers could expose standardized MCP tools.

Bevel Intelligence could then securely connect to compatible services—with explicit user authorization—and use the capabilities those services expose.

This could eventually allow Bevel Intelligence to interact with:

Digital gyms • cardio equipment • nutrition platforms • smart scales • workout applications • fitness services • other health technologies

while keeping Bevel Intelligence at the center of the experience.

The feature I would like to see

Allow Bevel Intelligence to act as an MCP client and connect to user-authorized external MCP servers/APIs, with both read and optional write capabilities.

Ideally, permissions would be granular so users could independently authorize:

* Reading workout/performance data

* Reading external health/fitness data

* Creating workouts

* Modifying workouts

* Logging information

* Other external actions

Write actions could require explicit confirmation from the user before Bevel performs them.

I think this could evolve Bevel Intelligence from an AI that primarily interprets the information available inside Bevel into an AI health and fitness orchestrator capable of using the user’s entire connected fitness ecosystem.

Bevel understands the user → Bevel Intelligence decides how to adapt → connected equipment executes the training → Bevel learns from the results → the next recommendation improves.

I don’t mean the bevel > claude/other AI access, i understand that’s maybe not possible for the buisness model. but i want to give it access to my freezer and pantry to know what to suggest better. for now i constantly have to say “use food i’ve logged before” but that’s not always something I have. And depending on what model you route to, it sometimes fixates on things and it’s impossible to get it to actually have helpful suggestions.