The Aurekai MCP server exposes 89+ tools across 9 capability families, giving AI assistants access to media processing, inference, pipelines, knowledge management, commerce, and more — via stdio or HTTP.

• Runtime: Async HTTP gateway (akai_api), auth/sessions (akai_auth), capability discovery (akai_capability), OpenAI-compatible proxy (akai_proxy), CLI dispatch (akai_cli), and change watching (akai_watch).

• Commerce: Access gates (akai_gate), payments/invoices (akai_pay, akai_ledger), usage metering (akai_meter), service pricing (akai_finance), and SLA tiers (akai_tier).

• Intake: Transcribe audio (akai_transcribe), ingest artifacts (akai_ingest), extract frames (akai_frame_extract), detect scenes (akai_scene_detect), demux video (akai_video_demux), and discover clips (akai_clips).

• Memory: FPQ embeddings (akai_fpq, akai_fpqx), vector search (akai_vec), model weight quantization (akai_quant), SAE feature dictionaries (akai_sae), and structured layer inference (akai_sli).

• Proof: Cryptographic proofs (akai_proof), data canonicalization (akai_canon), Merkle-DAG graphs (akai_graph), and content hashing (akai_hash).

• Reason: Physics simulations (akai_physics, akai_leapfrog), coroutine-native pipelines (akai_flow), inference control (akai_control), and feedback/learning (akai_learn).

• Wire: Telephony via FreeSWITCH (akai_tel), QUIC media relay (akai_moq), netlist runtimes (akai_net), and wire captures (akai_wire).

• Publish: Briefs (akai_brief), TTS narration (akai_narrate), artifact packing (akai_pack), distribution (akai_distribute), rendering (akai_render), social repurposing (akai_repurpose), and P2P via BitTorrent v2 (akai_swarm).

• Substrate: Content tagging (akai_tag), compression (akai_compress), artifact indexing/search (akai_index), entity resolution (akai_entity), document fragmentation (akai_fragment, akai_paragraph), and analytics queries (akai_query).

Additionally, the server provides:

• 13 aurekai:// resources for live data reads (queue stats, models, runtime data, etc.)

• 8 named prompt workflows for common multi-step tasks (e.g., audio → transcribe → brief → deliverable, Merkle lineage inspection, invoice generation, memory pack creation)

• Advanced MCP features: tool annotations (readOnlyHint, destructiveHint, idempotentHint), resource pagination, resource subscriptions, _meta proof propagation, and embedded resource outputs.