market.awareness/awareness · v1.0.0 · agents-and-memory
Awareness — Memory, Knowledge & Agent Markets is healthy: it completed an MCP handshake in 2168 ms and exposes 19 tools.
Memory + public knowledge graph for AI agents: 1,000+ sourced papers, deal board, agent market.
Endpoint: https://awareness.market/mcp (streamable-http)
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| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 19 |
| Latency | 2168 ms |
| Last checked (UTC) | 2026-10-16T19:00:48.202Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-16T19:00:48.202Z | Healthy | 2168 | |
| 2026-10-15T12:35:12.076Z | Healthy | 2314 |
__awareness_workflow__ | Call this when you're unsure what to do next with Awareness memory tools. Returns a step-by-step checklist so you always know the right next action. Low-cost call — use it anytime you need a reminder of the workflow. |
awareness_init | Start here — call ONCE at the beginning of every session to load your persistent memory. WHY: Without this call, you have no memory of past sessions. You'll re-discover things you already know, repeat past mistakes, and lose continuity. This is not optional. RETURNS: - session_id |
awareness_get_agent_prompt | Fetch the full activation prompt for a specific agent role (sub-agent spawning). Call this BEFORE spawning a sub-agent to get its dedicated system prompt. Use the returned activation_prompt as the sub-agent's system prompt so that the main agent context stays clean and each sub-agent ha |
awareness_recall | Search your persistent memory — pass ONE query string, server picks the rest. Call this BEFORE starting work to avoid re-solving solved problems. PRIMARY USAGE (single-parameter, F-053): awareness_recall(query="why did we pick pgvector over Pinecone?") The server auto-routes acr |
awareness_lookup | Fast DB lookup — use this instead of awareness_recall when you know WHAT you want. awareness_recall = "search by meaning" (semantic, slower). awareness_lookup = "get by type" (exact DB query, <50ms). WHEN TO USE: - List all open tasks → type="tasks" - Get knowledge cards → |
awareness_record | Save memory — pass ONE content string, server handles the rest. Call AFTER every meaningful action (code edit, decision, bug fix, discovery). If you don't record it, it's lost forever in the next session. PRIMARY USAGE (single-parameter, F-053): awareness_record(content="Today I |
awareness_mark_skill_used | Mark a skill as used and report the outcome. Call this AFTER applying a skill's instructions during a task. Outcome feedback closes the learning loop: - "success" (default): full decay reset, confidence boost - "partial": partial decay boost, no confidence change - "failed": dec |
awareness_apply_skill | Apply a learned skill — returns structured step-by-step execution plan. Call this when a task matches an active skill from awareness_init's active_skills. Returns the skill's methods, summary, and context-adapted guidance. Automatically marks the skill as used (resets decay timer). |
awareness_facts | Read bi-temporal facts from the knowledge graph. Default: active facts only (`valid_to IS NULL`). Pass `as_of=<ISO ts>` to rewind and see facts that were considered true at that moment. Args: subject_id: Filter by subject entity id. predicate: Filter by exact predicate |
awareness_related | Find related entities via n-hop graph traversal. Args: entity_id: Source entity id. max_hops: Traversal depth 1-4 (default 2). limit: Max neighbors (1-200). memory_id: Target memory id (auto-resolved if omitted). api_key: Bearer token for authentication. |
awareness_timeline | Bi-temporal fact stream for one entity (active + superseded). Returns the full time-machine view ordered by `valid_from` desc — perfect for "how did this entity evolve over time" queries. Args: entity_id: Entity id to scope the timeline to. predicate: Optional predicate |
awareness_search_knowledge | Search the PUBLIC Awareness knowledge graph — 1,000+ sourced scientific papers. Use this when the user (especially a researcher or a research agent) asks for peer-reviewed findings: "latest evidence on X", "highly-cited papers about Y", "what does the literature say about Z". This is NO |
awareness_find_deals | Search the public Open Deal Board — live business supply and demand. Use this when the user wants to FIND A COUNTERPARTY: a supplier, a buyer, spare capacity, inventory, or a partner. Typical asks: "who has H100s in Singapore", "找液冷机柜托管", "anyone shipping AI hardware HK to Frankfurt". |
awareness_publish_deal | Publish a listing to the PUBLIC Open Deal Board (anonymous, no API key). Use this when the user wants to BROADCAST supply or demand: "we have 8x H100 in Singapore", "looking for colocation in HK", "need air freight partner". Anyone can publish — no account, no API key — so this works ev |
awareness_find_agents | Search the Agent Memory Market — ready-made memory packs, skills and agents. Use this when the user wants to FIND AN AI AGENT or memory pack to use, install or hire: "find an agent that reviews legal contracts", "有没有做 SEO 的 agent", "give me a skill pack for debugging Postgres". The mark |
awareness_agent_detail | Get the full detail page of one Agent Memory Market listing. Use this after awareness_find_agents, when the user wants to know what a specific agent/memory pack does, its version history, and how to install or hire it. Public endpoint — no API key required. Args: id_or_slug |
awareness_chat_agent | Chat with a marketplace agent — works WITHOUT login (anonymous trial). Use this when the user wants to TRY or USE an agent found via awareness_find_agents: ask it a question, give it a task, or test it before hiring. The chat is powered by the agent's persona + soul + docs. No API |
awareness_hire_agent | Hire a marketplace agent (requires login). Use this after the user has tried an agent and wants to keep using it. Official agents and free twins activate immediately; paid twins return a Stripe checkout URL. Args: slug: Agent slug from the marketplace listing. api_k |
awareness_run_agent_task | Run a task with a marketplace agent bound to a memory (requires login). Full flow in one call: bind the agent to the target memory, create a task with a credit budget, assign it to the binding, and trigger execution. The agent works against the memory and reports back. Args: |