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Query model benchmarks, costs, HN signals, and tech registry data.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "AICIA AI Signal" MCP server from askskill: Run: claude mcp add --transport http 'io-github-jack-mi-aicia-ai-signal' 'https://aicia-mcp.lxsgocode123.workers.dev/mcp'
Use AICIA AI Signal to summarize benchmark performance and usage costs for several mainstream models, then compare their cost-performance tradeoffs.
A comparison summary of model benchmarks and costs to support model selection.
Use AICIA AI Signal to find notable recent Hacker News technology signals and summarize the key trends.
A summary of Hacker News signals related to current technology trends.
Use AICIA AI Signal to look up registry information for an AI technology or tool and present it as a brief note.
A concise summary of the technology or tool's registry information.
Researchers or product managers can use it to review benchmark and cost information when evaluating model options for an initial comparison.
Developers or researchers can use it to inspect Hacker News signals and quickly spot topics or technologies gaining attention.
When someone needs to confirm registry information about a technology or tool, they can use it to retrieve and organize reference details.
It is a closed-source remote MCP tool. The description says it provides model benchmarks, costs, Hacker News signals, and technology registry information.
Based on the provided description, it can query model benchmarks, costs, HN signals, and technology registry information. For more specific fields and coverage, see the source repository.
The available material only states that it is a closed-source remote MCP and does not include installation steps, authentication details, or runtime prerequisites; see the source repository for details.
Aggregate and query normalized AI, tech, and research news on demand.
Automate red-teaming and reliability audits for AI agents through MCP.
Look up AI model IQ scores, rankings, benchmarks, and methodology.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Compare AI models across providers by cost, performance, and capabilities.
Draft and propose Signal messages with explicit human approval before sending.