The request is generic ('recommendations', 'what to use') and does not name a product, platform, or constraint. Products below are those whose sources show recommendation-related capability, but the right choice depends on what kind of recommendations are needed (e.g. product/content recommendation engines versus AI model or vector-search tooling).
A question back: What do you need recommendations for: products or content for end users, cloud/infrastructure advice, AI models for your agents, or something else? Which platform or stack do you use?
- 1
Recommendations AIgoogle.comverified and signed
Google Cloud's recommendation engine exposes a predict method that returns recommendation predictions, with API key registration for authentication 12.
official:API · Recommendations AI (Beta)
for agents: recommendationengine.projects.locations.catalogs.eventStores.placements.predict · recommendationengine.projects.locations.catalogs.eventStores.predictionApiKeyRegistrations.create · recommendationengine.projects.locations.catalogs.eventStores.predictionApiKeyRegistrations.list
- 2
Azure Advisor Recommendations APImicrosoft.comverified and signed
Microsoft's AdvisorManagementClient lists cached Azure recommendations for a subscription and reports the status of recommendation generation 13.
official:API · AdvisorManagementClient
for agents: Recommendations_GetGenerateStatus · Recommendations_GetGenerateStatus · Recommendations_List
- 3
GitHubgithub.comverified and signed
GitHub's platform offers a github-mcp-server CLI and a model-recommendation action that analyzes chatmode or prompt files and recommends AI models by task complexity and cost 11.
official:CLI · github-mcp-serverskill · add-community-extensionskill · git-commitskill · acquire-codebase-knowledge+2,563 community tools built for it
for agents: model-recommendation · integrate-context-matic · custom_api_call
Sources
- 1GitHub · Change is constant. GitHub keeps you ahead. · GitHub · GitHubgithub.com · read 2026-10-29
- 2AI and Cloud Computing Services | Google Cloudcloud.google.com · read 2026-10-29
- 3Microsoft – AI, Cloud, Productivity, Computing, Gaming & Appsmicrosoft.com · read 2026-10-29
- 11model-recommendation: declares "model-recommendation" (the record we read and signed)unfragile.ai · read 2026-10-24
- 12Recommendations AI (Beta): declares "recommendationengine.projects.locations.catalogs.eventStores.placements.predict" (the record we read and signed)unfragile.ai · read 2026-10-24
- 13AdvisorManagementClient: declares "Recommendations_GetGenerateStatus" (the record we read and signed)unfragile.ai · read 2026-10-24
For agents: the 10 actions behind this answer
model-recommendation · model-recommendation verified and signed
Analyze chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency
recommendationengine.projects.locations.catalogs.eventStores.placements.predict · Recommendations AI (Beta) verified and signed
Makes a recommendation prediction. If using API Key based authentication, the API Key must be registered using the PredictionApiKeyRegistry service. [Learn more](https://cloud.google.com/recommendatio
Recommendations_GetGenerateStatus · AdvisorManagementClient verified and signed
Retrieves the status of the recommendation computation or generation process. Invoke this API after calling the generation recommendation. The URI of this API is returned in the Location field of the
· Arsenal Decision Engine website read, tools not yet checked
Query · Pinecone API verified and signed
Query
zedge/zoedepth · zedge/zoedepth verified and signed
yxzwayne/bge-reranker-v2-m3 · yxzwayne/bge-reranker-v2-m3 verified and signed
Newest balance-striking reranker model from BAAI. Outputs rank scores for query-doc pairs. FP16 inference enabled.
zedge/instantid · zedge/instantid verified and signed
deepseek-ai/deepseek-r1 · deepseek-ai/deepseek-r1 verified and signed
A reasoning model trained with reinforcement learning, on par with OpenAI o1
· Ritual's Discovery Engine website read, tools not yet checked
Ritual's discovery-to-recommendations workflow builds clarity before context debt slows you down.
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.