Semilattice - audience prediction is an MCP server listed on the M8ven Trust Index. M8ven has not graded it: we have no way to read this server ourselves. No publisher has claimed this listing.

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Semilattice - audience prediction

Test content, personalise features, and A/B test decisions with accurate audience prediction. Semilattice MCP lets agents predict things about your audience. Your agent could predict which email subject line or website copy your audience would prefer. Or it could predict which use cases your audience wants to see in a new product experience. Audience predictions are >87% accurate on average and take <20 seconds to complete.

0· listed on smithery

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Semilattice - audience prediction

Source: Smithery

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The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// quality suggestions

All four hints declared on every tool

4/4 tools missing one or more hints — list_population_models (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); create_prediction (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_prediction (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +1 more. OpenAI's directory rejects tools where any hint is missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

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// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 1 concrete improvement we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
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code hash: 6802bf34a23adee6adc647d8ad28fcda7c2e62762556d1f1d6e40e628cc03e87
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