Network AI
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TDQS
Scored across 24 tools
Each tool targets a distinct function: blackboard operations are separated into list/read/write/delete/exists/search, budget tools split status/spend/reset/set/log, token tools cover create/validate/revoke, and audit tools split query/tail. Even similar read-only tools (blackboard_search vs blackboard_list vs context_pack) have clear intended use cases described.
All tools follow a consistent snake_case pattern of domain_prefix_action, e.g., blackboard_list, budget_status, token_create, agent_spawn, config_get. The domain prefix is always lowercase and separated by an underscore, with clear verbs (list, read, write, delete, exists, status, spend, reset, set, get_log, create, validate, revoke, query, tail, set, list, spawn, stop, transition, info, pack, search).
With 24 tools, the server is on the heavier side but the scope is broad (orchestration, blackboard, budget, tokens, audit, agents, FSM). Each tool serves a distinct purpose and the count is justified for a complex orchestrator server, though a slightly tighter set could be possible.
The server covers core lifecycle operations for blackboard (create/read/update/delete/list/search) and tokens (create/validate/revoke), plus budget management (status/spend/reset/set/log) and basic agent operations (list/spawn/stop). Minor gaps include no direct agent removal or FSM reset, and no explicit tool for blackboard TTL extension, but agents can work around most gaps.