A
licenseNot graded
qualityB
maintenanceConnects Claude to AI/ML evaluation harnesses, token latency profilers, and prompt security red-teaming tools, enabling model benchmarking, latency profiling, and security assessment through MCP tools.
MIT
forge
by TT-Wang
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TDQS
A4.9/5.0
Scored across 7 tools
Disambiguation5/5
Each tool has a distinct, well-defined purpose with clear boundaries. For example, forge_logs is for event stream queries, iteration_state handles retry tracking, memory_recall/save manage learned patterns, session_state handles session persistence, and validate/validate_plan handle different validation scopes. The descriptions explicitly state what each tool should and should not be used for, preventing confusion.
Naming Consistency5/5
All tools follow a consistent snake_case naming convention with clear verb_noun patterns. Tools like forge_logs, iteration_state, memory_recall, memory_save, session_state, validate, and validate_plan maintain perfect consistency throughout the set, making them predictable and easy to understand.
Tool Count5/5
With 7 tools, this server is well-scoped for its forge orchestration domain. Each tool serves a specific, necessary function in the workflow (logging, state management, memory, validation), and none feel redundant or missing. The count supports comprehensive coverage without being overwhelming.
Completeness5/5
The tool set provides complete coverage for forge orchestration workflows. It includes logging (forge_logs), state management (iteration_state, session_state), knowledge persistence (memory_recall/save), and validation at both module and plan levels (validate, validate_plan). There are no obvious gaps—agents can manage the entire lifecycle from planning through execution to learning.
Maintenance
ActivityMaintained
ResponsivenessLimited data