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Glama vs Toolhouse

Toolhouse scores higher on the AgentReady, 79/100 against 56/100. They differ on 19 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Glama. Glama is a superset of the official MCP Registry that indexes, scans, and ranks MCP servers based on security, compatibility, and ease of use.

Toolhouse. Toolhouse allows everyone to create AI workers — specialized agents that are good at performing repetitive work autonomously and reliably.

Where Glama is ahead

Glama passes clear canonical domain, and Toolhouse does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: limits / constraints documented. Toolhouse misses it.

And on adopt, self-service signup and no mandatory sales call. Toolhouse misses those.

Finally, on operate, observable execution. Toolhouse misses it.

Where Toolhouse is ahead

Toolhouse passes clear product positioning, public docs discoverable and llms-full.txt / full agent docs, and Glama 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, openapi / spec quality, authentication documented, request examples provided, response examples provided and errors and status codes documented. Glama misses those.

And on adopt, agent-compatible signup flow, programmatic credential creation, copyable quickstart and official python sdk. Glama misses those.

Finally, on operate, machine-readable errors. Glama misses it.

What neither does

Both fail fast time to first request, cli available, 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. Toolhouse leads 93 to 67. Glama misses clear product positioning, public docs discoverable, llms-full.txt / full agent docs; Toolhouse misses clear canonical domain.

Understand is whether an agent can read the docs and work out how the API behaves before calling it. Toolhouse leads 92 to 31. Glama misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented; Toolhouse misses limits / constraints documented.

Adopt. Toolhouse leads 65 to 57. Glama misses agent-compatible signup flow, programmatic credential creation, fast time to first request, copyable quickstart, official python sdk, cli available; Toolhouse misses self-service signup, no mandatory sales call, fast time to first request, cli available.

Operate. Glama leads 67 to 65. Glama misses machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; Toolhouse misses retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution.

Pricing

Glama starts at $9/month and has a free tier. Toolhouse starts at $500/month with no free tier.

Signal by signal

Which to pick

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

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