feature-separate-eval-mcp
Related Servers
Alternatives to feature-separate-eval-mcp
No user-submitted related servers found.
Related Servers
- FlicenseCqualityCmaintenanceEnables users to analyze already-persisted single-patent evaluations to discover cross-patent batch issues such as language, technical-domain, input-structure sensitivity, and language-structure interactions, without modifying existing MCP, ledger, or single-record evaluation JSON.5-
- AlicenseCqualityDmaintenanceEnables acceptance gates for AI coding-agent runs by recording evidence, running deterministic validation, applying a quality gate, and rendering auditable outcomes.733 PyPIApache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to search and retrieve patent prosecution records, including full-text examination documents, examiner and applicant citations with AI/ML-enriched metadata, claim-level rejection data under 35 U.S.C. 101/102/103/112, and Commissioner final decisions on patent application petitions. It also supports raw-response passthrough for self-diagnosing upstream issues.230 npmMIT
- FlicenseNot gradedqualityAmaintenanceEnables patent-rule lookup with citations over MPEP/USC/CFR via hybrid vector, lexical, and reranked search, plus prior-art searching across 100M+ patents through Google BigQuery. Also runs automated 35 USC 112 compliance checks on draft claims and applications and generates patent-style diagrams and flowcharts.-
- AlicenseNot gradedqualityBmaintenanceEnables auditing coding-agent claims against local execution traces to detect contradictions, exposing session audits, recent failures, and optional scope-alignment triage.MIT
- FlicenseNot gradedqualityBmaintenanceEnables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.8-
TDQS
Scored across 3 tools
Each tool covers a distinct stage of the evaluation workflow: status lists pending records, check performs a pre-flight evaluability test, and run executes the actual evaluation. There is no meaningful overlap or ambiguity between them.
All tool names follow the same feature_separate_eval_ prefix plus a single clear verb: status, check, run. The naming pattern is entirely consistent and predictable.
Three tools is appropriate for this focused evaluation pipeline. Each tool provides a distinct, necessary function, and none are redundant or ornamental.
The tool set covers the full workflow from discovering the next pending record, to verifying evaluability, to running the evaluation and persisting results. No critical missing operation is evident for the server's stated purpose.