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741,893 tools. Updated 2026-11-04 10:26

"Finding the best memory MCPs for in-session management" matching MCP tools:

  • Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.
    ConnectorNo auth
  • Fetch the AI-maintained memory document for a project or workspace — the best single source for a handoff-style briefing. Sections include purpose, glossary, key people, activity digest, and routing signals, distilled across all meetings. Pass EXACTLY ONE of `project_id` (project memory) or `workspace_id` (workspace-level memory); get ids from `list_workspaces`. Returns the memory as rendered markdown plus `updated_at`. Start here for "give me a summary / bring me up to speed on project X" questions, then drill into `find_subjects`/`search_meeting_transcripts` for specifics.
    ConnectorOAuth
  • Mint a PROJECT-scoped management token (`er_mcp_`) for MCP and REST; it cannot authenticate relay traffic. Use it after create_project to configure a fresh project, or for any project you already own. Attenuated by design: the scopes must be a subset of THIS management token's own grant (`read` is always included), expiry is mandatory (1–90 days, default 30, never "never"), and the minted management token (being project-scoped) can never mint management tokens itself. `spend` is human-granted only: no management token, of any scope, can mint one carrying it. Mint a spend-scoped token from the project's panel instead. Requires an ACCOUNT-scoped management token and the `config` scope. Returns the plaintext exactly once; only its hash is stored.
    ConnectorNo auth
  • Update an existing memory by ID. Use mode replace (default) to patch fields, or append to extend content. Re-embeds only when content changes. The target workspace is always the one the memory itself belongs to (echoed in resolved_workspace); optionally pass workspace: <name> as a safety confirmation — the call fails if the memory is not actually in that workspace.
    ConnectorNo auth
  • Return usage metrics for the current MCP session: total tool calls, error count, aggregate duration and response size, per-tool call counts, and session timestamps. Metrics are in-memory and reset when the server restarts or the session goes idle. Use this for what this session has spent; for which toolsets exist at all use list_toolsets.
    ConnectorAPI key
  • Set up or restore your persistent memory connection. Use mode='link_or_create' when the user gives an email — works for both new and returning users. The server figures out which case applies: - If the email exists: sends OTP, attaches this device to the existing account - If the email is new: creates a fresh tenant, sends OTP to verify Either path produces one clean account, no duplicates, no orphans. Call setup_account(mode='link_or_create', email='their@email.com'). Then ask the user for the 6-digit code from their email and call setup_account(mode='verify', code='123456', email='their@email.com'). Passing email= on verify is required — session IDs rotate between requests on some clients. mode='restore': use when the user provides an emk_* API key directly. mode='reveal_key': echo the full emk_* API key. Works directly on an ALREADY-authenticated session; if the session isn't bound (some clients rotate or omit session ids between requests, so this can happen right after a successful verify), pass email= to receive a 6-digit code, then call again with email= and code= — the same rotation-proof lookup as mode='verify'. Success responses only show a key fingerprint (first 8 chars + last 4) — call this mode only when the user actually needs the full key, e.g. to persist it as 'Authorization: Bearer <key>' in an MCP connector config. Callers who prove neither a bound session nor email ownership are refused. If no email yet, ask: 'What email should I use for your memory account?' After setup completes, call search_facts as the first action to load prior context.
    ConnectorNo auth

Matching MCP Servers

Matching MCP Connectors

  • Search China Footnotes for sights and neighbourhoods in the Chinese cities it covers. Matches English or Chinese names, aliases, district, type and tags. Suited to questions about what to see or do in a city, and to finding a place's id from its name. With no arguments it lists places and the city ids covered; asked about a city it does not cover, it says so rather than returning unrelated places. Returns short summaries with ids and page links, best match first; matchedAll is false when no place matched the whole query and the results only share some words with it. Opening hours, tickets, booking and traveler tips come from get_place.
    ConnectorNo auth
  • Lints one DESIGN.md file with the official @google/design.md 0.4.0 linter, the same check as `npx @google/design.md lint DESIGN.md`. Best for: checking a DESIGN.md file the user pasted before it goes to a coding agent. Pass the whole file contents in text, including the YAML front matter between the --- lines; a file path or URL will not work, so ask the user to paste the file. Returns: each finding with its severity, rule and token path (broken token references, low text contrast, missing primary color, unused colors, section order, unknown keys), the error, warning and note counts, the sections found and the token counts. The file is valid when it has no errors. Front matter that is not valid YAML comes back as a finding, not an error. Do not use for: comparing two versions (use diff_design_md) or reading a website (use extract_design_md).
    ConnectorNo auth
  • Put something into LociHub: "capture this", "save this chat", "send this to my Stage". Adds to the owner's Stage or Sessions for review; nothing is overwritten or deleted. Not for reading or finding: ``search`` / ``hydrate``. ``choice`` is required and is the user's pick; ``capture_options`` returns the choices when the user hasn't said which. With a ``choice`` and its inputs it acts in one call. * ``choice="session"`` + ``turn``: the conversation -> the owner's Sessions. * ``choice="summary_session"`` + ``turn`` + ``content`` (a short recap of this conversation): the conversation -> Sessions, and the recap -> Stage. * ``choice="note"`` + ``content`` (+ optional ``title``, ``files``): content -> Stage for the owner to review. No session, no node, no address -- the owner decides what it becomes and where it lives. * ``choice="memory"`` + ``content``: facts about the owner that the user gave in this conversation, one per line -> one Stage item per fact. Only what was said in this conversation is captured; nothing from other conversations. No ``turn``. ``turn`` is the whole conversation, verbatim: the tool stores exactly the messages it is given as the session, so a summarized, shortened or reworded transcript is stored as such. A recap belongs in ``content``. It is a turn/v1.0 object (or a JSON string of one): {"schema": "turn/v1.0", "model": "<model>", "turns": [{"seq": 0, "role": "human", "content": "hi", "timestamp": "2026-06-09T18:20:02Z"}]} Each ``turns[]`` item: ``seq`` (0-based, +1), ``role`` (``human`` / ``assistant``, alternating, first ``human``), non-empty ``content``, ISO 8601 ``timestamp`` (non-decreasing). Re-capturing the same conversation is safe: the hub appends only the new messages. ``files``: a list of ``{"name", "content_type", "content_base64"}`` that ride a note into Stage and are saved with the node. ``summary`` is the old name for ``content``. The first capture in a chat has no ``conversation_id``; the reply carries the one the hub minted, and passing it again in the same chat appends to that session. ``status: "queued"`` means durably queued: it appears shortly.
    ConnectorOAuth
  • Pro/Teams. BLOCK until the session owner posts the next `steer` event to a Governed Session from AIDB Studio, then return it. DELIVERY GUARANTEE: the durable cursor read against the session log is authoritative (at-least-once: a lost response is safely re-issuable with the same cursor, and timed_out is only returned after a final confirming read). The in-between wake-up is a best-effort in-process push: usually sub-second, but a steer is never lost if a wake-up is missed; the confirming read catches it. See the after_event_id and timeout_s parameter descriptions for the semantics. THE LOOP: finish a task -> post me.session_event handoff -> call me.await_steer -> on a steer, FIRST post me.session_event event_type=ack ('Started: <steer gist>'), then execute, then handoff, then call me.await_steer again; on `timed_out`, call again with the returned after_event_id. REQUIRES team mode on the session (toggled by the owner in the web app); owner-scoped, so foreign session ids read as not found. Read-only: this tool never writes events. REJECTION CODES (invalid_request): 'Session not found.' (not yours, or no such id); 'Team mode is off for this session.' (owner enables it on the session page). Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.
    ConnectorNo auth
  • Pro/Teams. BLOCK until the session owner posts the next `steer` event to a Governed Session from AIDB Studio, then return it. DELIVERY GUARANTEE: the durable cursor read against the session log is authoritative (at-least-once: a lost response is safely re-issuable with the same cursor, and timed_out is only returned after a final confirming read). The in-between wake-up is a best-effort in-process push: usually sub-second, but a steer is never lost if a wake-up is missed; the confirming read catches it. See the after_event_id and timeout_s parameter descriptions for the semantics. THE LOOP: finish a task -> post me.session_event handoff -> call me.await_steer -> on a steer, FIRST post me.session_event event_type=ack ('Started: <steer gist>'), then execute, then handoff, then call me.await_steer again; on `timed_out`, call again with the returned after_event_id. REQUIRES team mode on the session (toggled by the owner in the web app); owner-scoped, so foreign session ids read as not found. Read-only: this tool never writes events. REJECTION CODES (invalid_request): 'Session not found.' (not yours, or no such id); 'Team mode is off for this session.' (owner enables it on the session page). Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.
    ConnectorNo auth
  • Semantically rank discoverable (interviewed) candidates against one of the employer's own jobs, with a per-candidate fit score AND a white-box explanation. WORKFLOW for finding the best hire: 1) call with tier:'best' to get the strongest candidates (cover the required skills + proven in interview), cascade to tier:'good' then tier:'weak' only if you need more (read tierCounts to decide; paginate within a band via page.hasMore, not page.total); 2) each row carries matchExplanation — the white-box 'why' (the fit score, the skills the candidate PROVED in their interview, what they're missing, and a plain-English rationale) — use it to explain your shortlist on OUR data, not a black box; 3) for the few you shortlist, call employer.get_candidate_evidence(jobId, userId) for the interview facts + Q&A to write a deeper comparative review. Omit tier for the full ranked pool (back-compat). Returns NOT_FOUND when the job is missing / owned by another employer (no existence leak), or NOT_INDEXED / NO_CATEGORIES when the job is not indexed for semantic search yet (re-save / republish, then retry).
    ConnectorNo auth
  • Persist a fact, decision or conclusion into the memory, attributed to you. It becomes retrievable via cortex_ask immediately, in this session and every future one, from any agent the user has connected. Provenance: pass source_universal_ids from a previous cortex_ask so the fact links to its evidence. Keep each memory to ONE self-contained fact. Use this whenever the conversation produces a durable conclusion the user would want remembered — a decision, a preference, an outcome, a commitment. If the result carries `choice.choice_required: true`, the memories source is waiting for the user’s enrichment choice: ask `choice.question`, offer exactly "Standard" or "Describe your goal", and record the answer with cortex_choose_enrichment.
    ConnectorNo auth
  • Look up ETFs by name, ticker or ISIN, with classification, listing, index, distribution-policy, AUM, expense-ratio and yield filters. Best for finding a known fund. For ranking questions ("cheapest", "largest", "best performing", "most liquid") prefer screen_etfs, which evaluates the whole universe: here minAum and minYieldTtmPct are applied only to a bounded profile-enriched candidate scan, so do not describe the result as exhaustive when candidateCapReached is true. Use get_etf_snapshot for one listing, get_etf_fund to resolve an ISIN across venues, and get_etf_holdings for constituents. Read-only.
    ConnectorNo auth
  • Remember something, with its sources and its knowledge time. Nothing is ever overwritten: saving a `factor_def` or `watchlist` under an existing key SUPERSEDES the previous version (both rows survive, so "what did I believe in June?" stays answerable), and saving identical content twice is a no-op rather than a duplicate. Args: kind: 'query' | 'factor_def' | 'watchlist' | 'finding' | 'note'. content: the thing to remember, as an object. key: the stable name — REQUIRED for 'factor_def' and 'watchlist' (that is what makes a definition reusable next session instead of re-invented). as_of: the knowledge time this memory is about. Recall can bound on it, which is what keeps a memory from leaking the future into a point-in-time question. source_query_ids: the `twmd_q_…` ids behind this. REQUIRED for 'finding' — a conclusion that cannot point at its data is not evidence, and will be refused. agent_id: optional label for which of your agents wrote this.
    ConnectorNo auth
  • IMPORTANT — bulk domain migration: domain moves via revise are for individual corrections only. If the user needs to move many memories between domains, inform the user that bulk migration must be performed via the admin interface (merge_domains) — do not attempt to replicate a merge by looping revise calls. Update one or more existing memories. Omitted fields are unchanged. Single: pass fields directly — returns {updated, connections, suggested_connections, applicable_standing_rules} and, when the filing-time threshold is crossed, possible_contradicts + possible_contradicts_candidates (same shapes as remember()). Batch: {items:[{id,...},...]} — returns {items:[{id, updated, connections, suggested_connections, applicable_standing_rules, ...}]} with a per-item envelope on each success, not only an updated count. After every successful revise — plain update, override, claim, or supersede — review applicable_standing_rules (domain-scoped standing rules, distance-independent), connections, suggested_connections, and possible_contradicts in the same turn; do not defer to a separate recall or suggest_connections call. Semantic similarity reflects aboutness, not agreement; the server surfaces candidates that may warrant your review but never asserts they conflict. Every memory has an owner (whoever created it) — revising a memory you don't own is rejected with error_class=forbidden unless you are an Editor or Owner and supply override_reason (required) plus override_confirm=true (required only when the memory is human-owned; not required for agent-owned or ownerless memories). The override path requires a session with a workspace role (a human JWT session, or a personal key linked to an Editor/Owner user); sessions on plain workspace keys cannot override regardless of arguments. An override changes content in-place, not ownership — substantive changes (label, description, why_matters, node_kind) on a foreign-owned memory are rejected; use supersede=true instead, which creates your successor memory, archives the original intact, and wires a supersedes relationship. supersede=true (single-memory form only) returns {superseded, archived_id, archived_connections} plus the revise envelope on the successor; foreign supersede uses the same override_reason/override_confirm ceremony. Correction-class overrides (tags, occurred_at, transient) may still use in-place override. override_reason/override_confirm/supersede apply to the single-memory form only — batch revise has no override or supersede path: if any item in the batch targets a memory you don't own, the whole batch is rejected and none of it applies; revise that item individually instead. claim=true (single-memory form only) makes an ownerless memory (owner_id IS NULL — either it predates ownership tracking, or was orphaned by a member offboard) yours: requires Editor or Owner role, is rejected with error_class=validation if the memory already has an owner (use override or supersede instead), and error_class=conflict if someone else claimed it first (race). claim never moves a memory from one owner to another — only from no owner to you — and may be combined with other field updates in the same call. domain (single-memory form only) moves the memory to a different domain; domain_move_reason is required when domain is present — the call is rejected with error_class=validation if domain_move_reason is absent; domain equal to the memory's current domain is also rejected. On failure, content[0].text is JSON: {"error_class": "not_found|conflict|retryable|forbidden|validation|internal", "message": "..."}. Switch on error_class: retry on retryable, surface message on validation, re-fetch on not_found.
    ConnectorAPI key
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 7,176 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 7,176 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 7,176 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 7,176 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth
  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 7,176 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
    ConnectorNo auth