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PopNx MCPAIを使う人のためのMCPサーバー · やさしい言葉で解説

INITE Brain

作者:inite-aiバージョン 2.7.0i★ –iクラウドで動く
レジストリ名io.github.inite-ai/inite-brain-service

インストール

概要

作者による説明(英語の原文)

Long-term memory for AI agents: a bitemporal knowledge graph with cited answers.

PopNx MCPの3つの柱

この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。

できること

このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。

  • 接続方法: クラウドで動く
  • トランスポート: Streamable HTTP
  • カテゴリ: 検索・AIの記憶

インストール

使っているAIアプリを選んで、設定をコピーしてください。

インストールの前に

必要なもの

どのアプリを使っていますか?

上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。

ウェブ、デスクトップ、モバイルのClaudeで使えます:

  1. Settings → Connectors を開く
  2. Add custom connector を押す
  3. 下のアドレスを貼り付けて Add を押す
  4. サインインを求められたら、作者のサービスにサインインする
SettingsConnectors1+ Add2https://…3
https://brain.inite.ai/mcp

ターミナルでこれを実行します

claude mcp add --transport http inite-brain-service https://brain.inite.ai/mcp

Cursorで開く

または、設定を自分でコピーする

プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます

{
  "mcpServers": {
    "inite-brain-service": {
      "url": "https://brain.inite.ai/mcp"
    }
  }
}

ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。

VS Codeで開く

または、設定を自分でコピーする

プロジェクトの .vscode/mcp.json に貼り付けます

{
  "servers": {
    "inite-brain-service": {
      "type": "http",
      "url": "https://brain.inite.ai/mcp"
    }
  }
}

ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。

ChatGPTがつなげるのは、インターネット上のサーバーだけです:

  1. ウェブ版のChatGPTを開き → Settings → Connectors (Apps)
  2. Create または Add を押す(プランによっては、先に Advanced で Developer mode をオンにする必要があります)
  3. 下のアドレスを貼り付け、名前を付けて保存する
  4. サインインを求められたら、作者のサービスにサインインする
SettingsConnectors1+ Create2https://…3
https://brain.inite.ai/mcp

メニューの名前は、プランや時期によって少し違うことがあります。

選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。

この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。

できること

ツールの名前と説明は、サーバーが2026年11月6日に返したそのままの内容です。書いたのは作者で、私たちではありません。プレーンテキストとして表示しています。

  • about_brain

    What this server is and what it can do, before you connect. Reads nothing and stores nothing.

  • how_to_connect

    The two ways to authenticate, and what each is for. Reads nothing and stores nothing.

  • search_knowledge

    Semantic search over the company knowledge graph: the facts the query is about, the links between their entities and what was left out. Apply asOf for historical "what was true on X" queries. Embedding model on this tenant: set per workspace.

  • search_multi_hop

    Planner-LLM decomposes the query into ≤ maxHops anchored sub-queries; later hops are anchored to the running entity set. Built for questions that combine evidence across turns / sessions, or that require reasoning over multiple entities ("tenants who complained in April AND upgra

  • graph_retrieve

    Resolves the named entities by canonical name, walks their 1-hop neighbourhood over knowledge_edge, and returns facts across seeds ∪ neighbours, optionally filtered by predicate hints. Works from entities named up front and returns what the graph holds around them ("who runs engi

  • synthesize

    Runs hybrid search then feeds the retrieved facts to a generator LLM that produces a citation-bearing answer (each claim ends with [factId]); a verifier LLM then judges whether every claim is supported. Three guardrail modes: strict (default) returns null on partial / unsupported

  • memory_diff

    Returns everything brain learned, unlearned, or replaced between two ISO 8601 cursors [from, to). createdFacts = new active facts; retractedFacts = facts marked retracted in-window with no successor; changedFacts = facts that were superseded by another (carries before+after); new

  • get_entity_profile

    One entity: canonical name, type and what brain holds about it now; detail "full" adds externalRefs, the ids of its records in the originating vertical.

  • get_entity_timeline

    Chronological audit of all facts brain has learned about this entity, including retracted ones. Useful for "what did we know when" investigations.

  • summarize_entity

    Returns a short one-line briefing about the entity — name, type, the most-confident active facts, external refs — suitable for dropping into an LLM context window. Caches in-process (per companyId / entityId / asOf / styleHint) so a hot entity touched across many turns doesn't re

  • get_competing_facts

    Returns facts in COMPETING status — those the conflict resolver couldn't auto-supersede because two same-predicate bitemporal facts overlap in valid-time and are too cosine-close within margin. Grouped by (entityId, predicate); 2-fact groups are pairs the resolver left for adjudi

  • detect_contradiction

    Dry-run preflight against fn::resolve_fact. Answers "if I were to record this fact right now, what would the resolver decide?" without writing to the database. wouldOutcome ∈ {INSERTED, SUPERSEDED, COMPETING, REJECTED}; reasoning explains which rule fired (semantics class, score

  • get_fact

    Read a single fact as stored — predicate (aspect), object (statement), confidence, validity window, source attribution (vertical / recorder / conversationId), and lifecycle state. Retracted facts still resolve (retracted: true) — "why did I stop remembering this" is part of the t

  • get_fact_provenance

    Return the grounding provenance behind one fact: the verbatim conversation turns it was derived from (episodes, chronological, with char-span quotes when the deriver stamped them). When the server runs with the recursive-closure flag the response additionally carries derivedFacts

  • find_related_entities

    Get entities connected to the given one via the knowledge graph.

  • match_procedure

    Cosine-matches procedural memory (curated 'how to' patterns the operator recorded) against a free-text context query. Returns top-K procedures sorted by similarity DESC then priority ASC. Surfaces the behaviour rules that apply to a context — e.g. "user asks about pricing" → "men

  • list_procedures

    Paginated listing of procedural memory for admin/review UIs. Sorted by priority ASC, createdAt DESC. Set includeRetired=true to see soft-deleted rows.

  • search_communities

    Cosine-matches the query against topic-community summaries — the coarse retrieval scope. A community is a cluster of related entities (label propagation over the entity graph) with one rolled-up summary. Suited to broad questions ("what do we know about X domain"): it returns an

  • list_communities

    Paginated listing of topic communities, largest first. Each row carries a label, a rolled-up summary, and the member count. Gives the knowledge map at a glance.

  • find_entity_communities

    Returns the topic communities a given entity is a member of. Places an entity in its broader context — which clusters / themes it participates in — and leads from one entity to the wider neighbourhood the cluster summarises.

  • why

    Return the design decisions, rationale, invariants and gotchas recorded against a code anchor — the non-derivable engineering "why" a parser cannot recover from source. The anchor is a SCIP-style symbol string ("pkg/namespace/symbol") or a file path ("repo/path/file.ts"). Pass as

  • recall_decisions

    Find recorded design decisions / rationale / invariants / gotchas by NL topic ("why do we resolve facts through one gateway?"), across ALL code anchors — a semantic search over code-memory, rather than one known anchor. Returns matches with their code anchor + kind, ranked by rel

  • get_source_reputation

    Return everything brain knows ABOUT a source (not its facts): the operator-declared identity (type, authLevel — authority in conflict resolution), the learned per-domain agreement rates (domain = predicate; null domain = the global blended rate; rates need >=8 samples before the

  • workspace_status

    The tenant this credential writes to (companyId, its display name, whether it is a personal workspace), what is already in its memory (entities, active facts, facts recorded in the last 7 days), how many Domain Packs are installed, for a user-bound connection who the user is in t

  • record_fact

    Records one fact about an entity directly; the memory settles it against the facts it holds (INSERTED / SUPERSEDED / COMPETING). Most facts arrive through ingestion.

  • link_entities

    Insert an edge between two entities. `kind` is the edge type — `identity_of` merges the `from` entity into `to` (cross-vertical identity reconciliation), other typed edges (`paid_for`, `mentioned_in`, `worked_with`, …) are surfaced in related-entity reads and contribute to PPR /

  • retract_fact

    Mark a fact as no longer believed. Cascades to facts derived from this one. Does not delete; the row remains for audit.

  • record_procedure

    Record a 'how to' pattern that is surfaced when a similar context appears later. trigger = the context phrase the rule should match against (e.g. "user asks about pricing"); action = the behaviour to apply (e.g. "mention they're on platinum tier; they get 20% off"). priority orde

  • retire_procedure

    Mark a procedural memory row as retired (sets retiredAt). A retired procedure is no longer matched or listed by default. For a rule that no longer applies — distinct from a hard delete because the row stays for audit.

  • record_decision

    Capture the non-derivable engineering "why" about a code location: a design decision, its rationale, an invariant that must hold, or a gotcha. Anchored to a SCIP-style symbol or file path (NOT line numbers — survives edits). Stored as a bitemporal typed fact: re-recording a `deci

基本チェックi

誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月8日にチェック

  • ✓ 合格

    作者とリポジトリが一致

    レジストリ名は、リポジトリを所有するGitHubアカウント inite-ai で確認済みです

  • … 未チェック

    今もメンテナンスされている

    このリポジトリはまだGitHubから読み取っていません

  • … 未チェック

    ライセンスがある

    このリポジトリはまだGitHubから読み取っていません

  • ✓ 合格

    エンドポイントが応答する

    589ミリ秒で応答(HTTP 200)

  • – 対象外

    OAuthでサインイン

    サインイン不要

  • ✓ 合格

    ツールの説明に隠れた指示がない

    30件のツールの説明を読み、AIへの隠れた指示は見つかりませんでした

検索・AIの記憶のほかのサーバーとの比較i

  • このカテゴリのほかの5671件のうち0%よりスターが多い
  • このカテゴリのほかの5671件のうち89%より最近レジストリに公開
  • 基本チェックの結果が、このカテゴリのほかの5671件のうち100%と同じかそれ以上

レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。

よくある質問

INITE Brainは安全ですか?

当てはまる基本チェック3項目のうち3項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。

どんなキーやコードが必要ですか?

レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。

パソコンで動きますか、それともクラウドですか?

作者のクラウドで動きます。インストール不要でインターネット経由でつながりますが、送った内容は作者のサーバーを通ります。

今もメンテナンスされていますか?

GitHubリポジトリの最終更新は–、最新バージョン(2.7.0)がレジストリに届いたのは5 日前です。

どのアプリで使えますか?

Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。URLでつながるので、リモートMCPサーバーに対応したアプリ(開発者モードのChatGPTなど)でも使えます。

代わりに使えるものは?

同じカテゴリには:Context7, Codebase Memory, Exa

代わりの候補

同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。

カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i

Context7

作者:upstash

Up-to-date docs for AI agents

パソコンでもクラウドでも★ 64,224MIT昨日更新
インストール

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

パソコンで動く★ 48,148MIT21 日前更新
インストール

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

クラウドで動く★ 5,213MIT2 か月前更新
インストール

作者のREADME

READMEを開く

作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。

INITE Brain

Open-source memory for AI agents: a bitemporal knowledge graph with provenance, over MCP and REST.

Website · Quick start · Architecture · Domain Packs · Getting started · Docs · Agent guide

Quick start

Needs Docker with Compose v2, openssl, and an OpenAI API key (the default model configuration). Runs the released image (linux/amd64 and linux/arm64) with SurrealDB; no clone, no build.

: "${OPENAI_API_KEY:?export OPENAI_API_KEY first}"

mkdir inite-brain && cd inite-brain curl -fsSLO https://raw.githubusercontent.com/inite-ai/inite-brain-service/main/docker-compose.quickstart.yml BRAIN_KEY="brain_$(openssl rand -hex 24)" KEY_HASH="$(printf %s "$BRAIN_KEY" | openssl dgst -sha256 -r | cut -d' ' -f1)" cat > .env <<EOF BRAIN_VERSION=2.4 OPENAI_API_KEY=$OPENAI_API_KEY BRAIN_API_KEYS='[{"keyHash":"sha256:$KEY_HASH","companyId":"co_demo","scopes":["brain:read","brain:write"]}]' SURREALDB_PASSWORD=$(openssl rand -hex 24) SURREALDB_SCOPED_PASS=$(openssl rand -hex 24) FORGET_HMAC_KEY=$(openssl rand -hex 32) EOF docker compose -f docker-compose.quickstart.yml up -d --wait export BRAIN_URL=http://localhost:3030 BRAIN_KEY curl "$BRAIN_URL/health" echo "API key (shown once, only its hash is stored): $BRAIN_KEY"

Then remember something and ask about it, or point an MCP client at http://localhost:3030/mcp with Authorization: Bearer $BRAIN_KEY. BRAIN_VERSION takes X.Y (newest patch of that release) or vX.Y.Z; :latest is overwritten by the main-branch deploy. The first start downloads the local ONNX models into a volume, so the first write can take a few minutes; later ones do not. Hosted service, running from source, and production: Getting started.

Why not Mem0, Zep or Letta?

  • Mem0 is quicker to adopt: an add/search API, a managed platform and many framework integrations. Brain asks for more up front (a tenant, scoped keys, userId on personal memory) and in exchange keeps both valid time and knowledge time on every fact. A conflicting write supersedes the earlier fact or is parked as COMPETING; it does not silently overwrite.
  • Zep (Graphiti) is the closest design: Graphiti is also a temporal knowledge graph, and you can embed it as a Python library over a graph database. Choose it if you want memory inside your own process. Brain is a service: a separate database per tenant, API keys scoped to read, write and admin, PII controls, and GDPR forget_entity with tombstones.
  • Letta is an agent runtime in which the agent edits its own memory. Use it when you want the agent loop and its memory in one server. Brain is memory only: the agent stays in Claude Code, Cursor, Codex or your own code and reaches Brain over MCP (Streamable HTTP, or the stdio shim @inite/brain-mcp).
  • Answers can abstain. synthesize ends each claim with the id of the fact it rests on, a verifier checks the support, and strict mode returns no answer rather than an unsupported one. Each fact keeps a pointer to the conversation or document it came from.
  • No borrowed benchmark numbers. We do not set our memory-accuracy results against other systems' self-reported scores, which mostly use lenient judges. How we measure: docs/eval-protocol.md.

Part of the INITE Triad: Brain verifies the tokens INITE Auth issues, as INITE Billing does; the contract between them is the Triad contract. Run all three with one compose file: inite-ai/inite-triad.

What Brain is

Brain gives an agent memory between conversations: what was said, what changed, which entities are involved, and what supports an answer. A bitemporal graph stores structured facts and relationships; source episodes preserve context; scenes and beliefs capture events and evolving state. Domain Packs adapt that memory to a product's vocabulary and lifecycles without forking the engine.

Run Brain on your infrastructure or connect to the hosted service at brain.inite.ai. The web app provides memory search and exploration; REST and MCP provide the integration surface.

Why Brain

  • Memory with its context. Retrieve structured facts, source passages and, when enabled, scenes and beliefs. Follow provenance back to the supporting conversation, document or evidence fragment.
  • Changes stay inspectable. Facts carry valid time and knowledge time. Updates can supersede earlier facts; unresolved disagreements remain COMPETING. Retraction preserves history; administrative forgetting deletes associated records and leaves tombstones.
  • A model of your domain. Packs define typed predicates, extraction guidance, scene schemas, state transitions, verification and retention hints, and optional media capabilities and MCP tools.
  • Scope is explicit. Each tenant has a separate database. Personal memory uses userId; omitting it selects tenant-global memory. Key scopes, ABAC policies and PII controls further constrain access.
  • Answers can be checked. Hybrid search, graph retrieval and multi-hop gather evidence. Synthesis cites it and runs verification; strict guardrails can abstain when support is insufficient.

Architecture

Brain separates source material, derived memory and the read path. This is a conceptual view; individual ingestion routes and retrieval profiles activate different parts of it.

flowchart LR

input["REST / MCP conversations · documents · facts"] raw[("Source records documents · episodes · evidence")] derive["Indexers and scene builders candidates · entity resolution · conflicts"] memory[("Connected memory on SurrealDB facts + graph · scenes · beliefs")] retrieve["Retrieval semantic + lexical · graph · multi-hop"] answer["Synthesis + verification answer with citations or abstention"] packs["Domain Packs vocabulary · memoryModel · tools"] media["Evidence registration / upload scan hook · trusted processors"] input --> raw input --> derive media --> raw raw --> derive derive --> memory memory --> retrieve raw --> retrieve retrieve --> answer packs -. "domain configuration" .-> derive packs -. "capabilities and consent" .-> media

Memory layerWhat it preserves
Facts and entitiesTyped claims, canonical entities, relationships, confidence, source attribution and temporal history.
EpisodesSource turns and conversation context that derived memory can point back to or be rebuilt from.
ScenesBounded events with gists, entities, state changes and source links. Optional gist embeddings and a scene retrieval lane make them searchable alongside other evidence.
BeliefsDerived state with revision history and supporting scenes. Pack-projected state deltas can feed belief promotion.
EvidenceAssets, located fragments, processing runs and derived representations, with links to the memories they support.

Rebuildable memory. Derived worlds can be rebuilt from retained episodes in a staging version. Readers stay pinned to the previous world until promotion. Scene maintenance can process dirty conversations on a schedule, with budgets, leases and metrics; it can compose scenes and promote supported beliefs.

Configurable retrieval. Retrieval profiles select mechanisms for the corpus and question. Semantic and lexical search, graph expansion, reranking, raw passages, scene retrieval and belief retrieval are distinct capabilities. A known entity can enter through graph_retrieve; a question spanning relationships can use search_multi_hop.

Separate serving and background work. The NestJS service uses a SurrealDB-backed job queue, leases and worker-thread offloads. Run the image as one process or split PROCESS_ROLE=api|worker; see the operations guide.

The code includes capabilities that are opt-in, not a promise that every installation runs every layer. Scene construction, scene serving, belief promotion, evidence upload/processing and pack projections have separate gates. Check the architecture, operations and flag definitions for scenes, evidence and pack projections when enabling them.

Domain Packs

A pack is a versioned JSON manifest installed per tenant. It tells the engine how to interpret a domain's material. Pack-derived observations remain candidates for the core pipeline to validate and resolve.

A pack can declarePurpose
predicatesTyped vocabulary, value constraints, conflict semantics, decay and PII classes.

| extractionProfile, `evalFi

冒頭だけを表示しています。続きはリポジトリで読めます

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