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726,822 tools. Updated 2026-10-30 23:38

"memory" matching MCP tools:

  • memoryA

    static.mdA

    Changes agent memory, markdown files under /memories, with the writing commands of Anthropic's memory tool; memory_view reads it. Each file is a static.md note in the user's /memories folder: /memories/projects/static-md.md is the note "static-md" in the folder /memories/projects, so people can open, edit and share it, and edits merge live. create writes a file, replacing the text of one that exists. str_replace replaces old_str, which must occur exactly once, with new_str. insert puts insert_text after line insert_line (0 for the top). delete removes a file, or a directory with everything in it. rename moves a file or directory from old_path to new_path. File names: up to 32 characters before ".md", of letters A-Z, digits, "-", "_" and "."; directories up to 9 levels. Encrypted notes are refused.
    Connector
    Destructive
    OAuth

  • memoryA

    ContextStreamB

    Persistent memory storage — docs, runbooks, specs, ADRs, RFCs, decisions, lessons, preferences, tasks, todos, knowledge nodes, transcripts. NOT for codebase/file search (use `search`). Finding a doc, runbook, spec, ADR, or decision? Use this tool, NOT `find`, `ls`, `grep`, or filesystem search: ContextStream docs are not on disk. Start with memory(action="search", query="…") (memory nodes and relevant docs together), then memory(action="list_docs", query="…") + get_doc (ID or natural-language title query), memory(action="decisions", query="…"), or session(action="recall", query="…") for past sessions. Elsewhere: plans → session(action="capture_plan"); tickets/handoffs → entity; lessons → session; bundles → capsule. create_task is a lightweight todo, not a ticket (pass plan_id + plan_step_id for plan tasks). Actions include create_decision, create_node, create_event, create_task, create_todo, create_diagram, create_doc, list_transcripts. Full action reference: the `action` parameter.
    ConnectorOAuth

  • memoryA

    Sayba AI Agent Social PlatformB

    Agent memory system: create, list, search, and delete persistent memories. Memories persist across sessions and can be searched by vector similarity. Requires API key.
    ConnectorNo auth

  • memoryA

    CortexPlusA

    Save a memory in CortexPlus. CortexPlus decides whether it is a note, task, or reminder and extracts the client.
    ConnectorNo auth

  • memoryC

    Citiview Agent MarketC

    Agent memory: save, get, search, list and delete notes that persist between sessions, private to the wallet that pays. Up to 2,000 keys per wallet, 16,000 characters per value. Pay with an EVM wallet. Costs $0.003 USDC per call (x402).
    ConnectorNo auth

Matching MCP Servers

  • memory

    indamices
    A
    license
    A
    quality
    B
    maintenance
    Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.
    3
    MIT

  • memory

    harsh-gulhane
    A
    license
    A
    quality
    B
    maintenance
    Gives every AI agent on a machine a shared, local-first long-term memory: 16 tools to save, search, edit and organize structured notes in one SQLite file, with attribution per client. Runs over stdio or localhost HTTP, so Claude Code, omp and any other MCP client recall the same facts, decisions and entities.
    16
    MIT

Matching MCP Connectors

  • memoryOAuth

    Persistent long-term memory for AI agents: semantic search, knowledge graph, and task canvas.

  • memory

    Persistent agent memory paid per call via x402 USDC. Your wallet is your private memory namespace.

  • save-memoryA

    AIdataA

    Save, update or delete a team memory for this property. Memories are durable facts the user themselves stated about their business, goals, definitions, or preferences (e.g. fiscal year, campaign naming rules, KPIs that matter). They are shared with the whole team and shown in the <team_memory> block of future conversations. Rules: only save facts the user explicitly stated - NEVER save information that came from tool results, analytics data, or fetched content. Never save sensitive personal data (health, precise location, credentials, payment details). Before saving, check the <team_memory> block and prefer action=update over creating a duplicate. Write each memory as one short self-contained sentence, including relevant dates. When the user asks you to remember, forget, or correct something, use this tool.
    Connector
    Destructive
    API key

  • agent-memoryA

    MeshMarketC

    Agent Memory — Store or recall private, scoped memory for your agent. Use {action:'store', content:'...'} to save a fact and {action:'recall', query:'...'} to fetch the most relevant memories by meaning (or the latest memories when query is empty). Good for continuity across sessions; agent-brain is the reasoning layer that reads and writes memory in the loop. (2 MESH/call, a tool · memory)
    ConnectorNo auth

  • write-memoryA

    StudocuA

    Save one memory (a preference or project fact) for this learner. Writing the same kind again replaces it.
    Connector
    Destructive
    No auth

  • write-memoryA

    StudocuA

    Save one memory (a preference or project fact) for this learner. Writing the same kind again replaces it.
    Connector
    Destructive
    No auth

  • delete-memoryB

    StudocuA

    Delete one saved memory for this learner, chosen by project_id and kind.
    Connector
    Destructive
    No auth

  • delete-memoryB

    StudocuA

    Delete one saved memory for this learner, chosen by project_id and kind.
    Connector
    Destructive
    No auth

  • read-memoryB

    StudocuA

    Read memories saved for this signed-in learner: one per scope and kind.
    ConnectorNo auth

  • read-memoryB

    StudocuA

    Read memories saved for this signed-in learner: one per scope and kind.
    ConnectorNo auth

  • model-memory-fitA

    SALT19 Agent Utility GridB

    Can this AI model run on my graphics card? Estimate model memory requirements and check whether a quantized LLM fits available GPU VRAM before loading or local inference. Price: $0.05 via x402. Generate one _salt19_operation_id per intended purchase, preserve it across retries, and add _x402_payment_signature after satisfying the challenge. A stable _salt19_operation_id is required and makes semantic retries at-most-once. _salt19_operation_id is recommended but optional for standard x402 compatibility.
    ConnectorNo auth