Learning Adapter
Adaptive proxy server that intelligently optimizes MCP tool responses by learning which data fields are most valuable and filtering out noise to reduce token usage by up to 80%. Uses OpenAI's API to automatically analyze tool outputs and classify fields into essential identifiers, useful data, and technical metadata, then applies smart masking to return only relevant information while providing access to hidden fields on demand through an 'include' parameter. Designed to federate multiple MCP servers while dramatically reducing response sizes for token-sensitive workflows, with automatic context injection and persistent learning that improves filtering accuracy over time.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/sivachow/mcp-learning-adapter
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Learning Adapter from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.