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Make vs SavvyCal

Make scores higher on the AgentReady, 62/100 against 39/100. They differ on 13 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Make. Make is a visual AI automation platform that enables users to connect any app, data source, or AI model to build and manage automations and AI agents visually, in code, or via natural language prompts.

SavvyCal. A scheduling platform that provides a fresh way to find a time to meet, with flexible controls to keep calendars sane and an ultra-convenient booking experience for recipients.

Where Make is ahead

Make passes search discoverable, llms.txt published, llms-full.txt / full agent docs and mcp discoverable, and SavvyCal does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: structured api reference. SavvyCal misses it.

And on adopt, official typescript sdk, official python sdk and mcp integration available. SavvyCal misses those.

Finally, on operate, structured, predictable output and agent compatibility verified. SavvyCal misses those.

Where SavvyCal is ahead

SavvyCal passes authentication documented, and Make does not. That is understand, whether an agent can read the docs and work out how the API behaves before calling it.

It also holds adopt: no mandatory sales call and agent-compatible signup flow. Make misses those.

What neither does

Both fail clear canonical domain, clear product positioning, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, programmatic credential creation, cli available, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable. If your agent needs any of those, you will be building it yourself either way.

Score, pillar by pillar

The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.

Discover is whether an agent can find the product at all without being told it exists. Make leads 80 to 40. Make misses clear canonical domain, clear product positioning; SavvyCal misses search discoverable, clear canonical domain, clear product positioning, llms.txt published, llms-full.txt / full agent docs, mcp discoverable.

Understand. Make leads 38 to 31. Make misses openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; SavvyCal misses structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt. Make leads 65 to 50. Make misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, cli available; SavvyCal misses programmatic credential creation, official typescript sdk, official python sdk, cli available, mcp integration available.

Operate. Make leads 65 to 35. Make misses machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; SavvyCal misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified.

Pricing

Make does not publish a machine-readable starting price and has a free tier. SavvyCal starts at $10/user/mo and has a free tier.

Signal by signal

Which to pick

Make clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: Make and SavvyCal. Alternatives to each: Make, SavvyCal.

An agent can fetch this as data: POST /v1/compare {"slugs": ["make", "savvycal"]}