프로토타입 · 미리 보기: 공식 레지스트리의 모든 서버를 담고 있으며 30분마다 동기화해요. 리뷰와 계정 기능은 다음 단계에서 열려요.
PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

Prism Coder

제작자: dcostenco버전 20.22.4i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.dcostenco/prism-coder

설치

개요

제작자가 쓴 설명(영어 원문)

Session memory for coding agents: local-first recall, drift detection, on-device inference.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 검색 및 AI 기억
  • 입력해야 할 값: BRAVE_API_KEY, GEMINI_API_KEY, SUPABASE_KEY

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 직접 입력할 값: BRAVE_API_KEY, GEMINI_API_KEY, SUPABASE_URL, SUPABASE_KEY, PRISM_USER_ID, GCP_PROJECT_ID, VERTEX_LOCATION, VERTEX_DATA_STORE_ID

비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.

필요한 것

  • npmNode.js 설치(npx 명령어용)

어떤 앱을 쓰세요?

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

{
  "mcpServers": {
    "prism-coder": {
      "command": "npx",
      "args": [
        "-y",
        "prism-mcp-server"
      ],
      "env": {
        "BRAVE_API_KEY": "your-value",
        "GEMINI_API_KEY": "your-value",
        "SUPABASE_URL": "your-value",
        "SUPABASE_KEY": "your-value",
        "PRISM_USER_ID": "your-value",
        "GCP_PROJECT_ID": "your-value",
        "VERTEX_LOCATION": "your-value",
        "VERTEX_DATA_STORE_ID": "your-value"
      }
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add prism-coder --env BRAVE_API_KEY=your-value --env GEMINI_API_KEY=your-value --env SUPABASE_URL=your-value --env SUPABASE_KEY=your-value --env PRISM_USER_ID=your-value --env GCP_PROJECT_ID=your-value --env VERTEX_LOCATION=your-value --env VERTEX_DATA_STORE_ID=your-value -- npx -y prism-mcp-server

실행하기 전에 빈 값을 채워 주세요.

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "prism-coder": {
      "command": "npx",
      "args": [
        "-y",
        "prism-mcp-server"
      ],
      "env": {
        "BRAVE_API_KEY": "your-value",
        "GEMINI_API_KEY": "your-value",
        "SUPABASE_URL": "your-value",
        "SUPABASE_KEY": "your-value",
        "PRISM_USER_ID": "your-value",
        "GCP_PROJECT_ID": "your-value",
        "VERTEX_LOCATION": "your-value",
        "VERTEX_DATA_STORE_ID": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "prism-coder": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "prism-mcp-server"
      ],
      "env": {
        "BRAVE_API_KEY": "your-value",
        "GEMINI_API_KEY": "your-value",
        "SUPABASE_URL": "your-value",
        "SUPABASE_KEY": "your-value",
        "PRISM_USER_ID": "your-value",
        "GCP_PROJECT_ID": "your-value",
        "VERTEX_LOCATION": "your-value",
        "VERTEX_DATA_STORE_ID": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

직접 입력할 값

  • BRAVE_API_KEY 비밀 env
    Brave Search API key for web search tools
  • GEMINI_API_KEY 비밀 env
    Google Gemini API key for AI-powered summarization and embeddings
  • SUPABASE_URL env
    Supabase project URL for session memory and knowledge persistence
  • SUPABASE_KEY 비밀 env
    Supabase service role key for database access
  • PRISM_USER_ID env
    User ID for multi-tenant row-level security isolation (defaults to 'default')
  • GCP_PROJECT_ID env
    Google Cloud project ID for Vertex AI Discovery Engine integration
  • VERTEX_LOCATION env
    Vertex AI location/region (defaults to 'global')
  • VERTEX_DATA_STORE_ID env
    Vertex AI Discovery Engine data store ID for enterprise search

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 dcostenco 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 7236개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 7236개 중 72%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 7236개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Prism Coder, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

입력해야 할 값: BRAVE_API_KEY, GEMINI_API_KEY, SUPABASE_KEY

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(20.22.4)은 14일 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT3일 전 업데이트
설치

Codebase Memory

제작자: DeusData

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

내 컴퓨터에서 실행★ 48,148MIT20일 전 업데이트
설치

Exa

제작자: exa-labs

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

클라우드에서 실행★ 5,213MIT44일 전 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

Prism Coder

Give your AI agent memory that lasts — and see the cloud tokens it never had to spend. Persistent sessions, knowledge graphs, offline tool-routing, and an auditable savings meter. Fully local and free.

Current release: v20.22.5 — local oMLX images and memory accounting

Choose an independent image model from discovered local inventory. Compatible

oMLX servers support controlled PNG image requests with local screening and

generation. Image accuracy depends on the model; Ollama image execution remains

unavailable for the saved image choice. See the oMLX guide.

*Local AI dashboard development preview. Screenshots below use an isolated demo with illustrative inventory and responses; they do not show a live account or prove model accuracy. The npm release may show an earlier interface.*

Prism Coder is an MCP server that gives Claude, Cursor, and other AI tools long-term memory that survives across sessions. It ships with the open-weight prism-coder model fleet (2B–27B) for fast, offline tool-routing — no cloud required. And it keeps score: every call served locally is metered, so prism savings shows the token volume that never reached your cloud model — measured honestly, in tokens.

No account needed. No API keys. Runs on your machine. A paid subscription adds cloud sync, higher model tiers, and team features through the Synalux portal.


What Prism gives you

  • Session memory that survives restarts — resume projects with handoff notes, recent work, open TODOs, and configurable quick, standard, or deep context.
  • Local-first inference — local Ollama models use automatic 2B/4B/9B/27B selection based on installed models, available RAM, context fit, and subscription entitlements. Eligible text-only coding can prefer configured oMLX; explicit task choices override the saved backend preference.
  • A savings meter you can audit — prism savings (or the local_savings tool from any host) reports the token volume local serving kept off your cloud model: headline, local share, per-model breakdown. It reports tokens, never an invented dollar figure, and prints its assumptions and known undercounts inline — a number you can check, not marketing.
  • Route-output enforcement — route mode returns only well-formed calls to tools the host actually advertised. Standard and higher plans can add authenticated deterministic correction; route_guard: "local" disables that correction only. cloud_fallback: false forbids cloud inference fallback and verify: false (with no evidence) disables the grounding verifier. With all three off, no request carries your prompt, draft or evidence; the per-call entitlement check and telemetry still contact the portal and carry neither.
  • One setup for every agent — prism connect configures Claude Code, Claude Desktop, Cursor, Gemini CLI, and Codex while preserving unrelated settings.
  • Subscription-aware skills — entitled skills are synchronized before the host launches, with safe upgrades, downgrades, conflict preservation, and offline last-good recovery.
  • Hook-free startup — MCP metadata and native instructions request Prism's startup context without requiring lifecycle hooks or a Prism-owned launcher. Where a host offers hooks (Claude Code, Codex), prism connect adds two small ones on top: mid-session prompt routing, and a post-compaction re-injection of the protected-floor digest.
  • Prompt-triggered Factory — enabled by default for suitable bounded implementation requests with explicit files, an allowed write list, and a verification command. The host starts and monitors the recommended pipeline; BCBA requests stay with the host. A dashboard toggle or PRISM_DARK_FACTORY_ENABLED=false disables Factory after an MCP restart. Try the three-test filename fix.
  • Safe escalation and observability — inference outcomes are explicit, reserved text remains fail-closed (clinical images are processed locally, never sent to the cloud), and local/cloud usage is recorded for review.
Local AI in the dashboard

Run prism dashboard, open Account & Settings → Local AI, then work through Connection → Models → Test. These controls configure local inference; session memory continues to work without installing a model.

StepWhat you doWhat to expect
1. ConnectionChoose an existing oMLX server or a Prism-managed installation, then Preview connection.Review the effect on coder, reviewer and image assignments before Use this server switches the shared oMLX connection. Saved model IDs are preserved.
2. ModelsChoose coder and reviewer models for each backend and Save coding preference. Save the separate Image model form independently.Saving records a choice; it does not download or load weights. Missing saved IDs stay visible instead of being silently replaced.
3. TestSelect backend and purpose, then Test saved model.A small request may load the saved model. The result reports execution or a blocking reason, with no cloud inference fallback.

Coding and review. Auto prefers configured oMLX for eligible coding. You can choose Ollama instead; explicit task choices take precedence. oMLX coding and review need the integration's separate text safety classifier. Local drafts and reviews still need host inspection and tests.

Images have their own choice. Select an oMLX vision model under Image model, then Save image preference. It may be the same model used for coding, but the setting is independent. Refresh image models reads metadata without loading a model. Vision metadata and RAM estimates are advisory; each request repeats compatibility and resource checks. The saved Ollama image option currently refuses execution; leave the image backend Not selected to retain existing image routing.

Read image-test results in two parts. The built-in diagnostic sends a red square. Reference facts describe that known PNG; AI observations contain the model's answer. A wrong answer cannot change the reference. A passing test proves execution, not visual accuracy. User images do not receive catalog facts from this diagnostic. Starting another test or changing backend or purpose clears the previous image result.

Server setup is optional and separate from npm. On supported Apple Silicon Macs, expand Install or update a Prism-managed server to install the server without downloading the optional coding starter pack. Add models through Open oMLX model manager. Installing a server update does not restart it or switch your connection. Restart managed server interrupts local inference and is available only when Prism can establish managed ownership and idle activity. External servers use their own upgrade and restart procedure.

Updating the Prism npm package or restarting your AI client does not upgrade an external oMLX server. If image status says Server update required, follow the existing-server upgrade guide. Tested Prism-compatible source identifies the compatible revision; Latest upstream releases is for release notes and may lack Prism's routes. See the oMLX guide for requirements and troubleshooting.

Get started

Memory search automatically applies ACT-R ranking: relevance plus recency, frequency and memory associations. No upvote or prompt instruction is required. To opt out, use Account & Settings → Preferences → Automatic Memory Ranking (ACT-R), then restart your AI client. PRISM_ACTR_ENABLED=false also disables it. This ranking is separate from the neural graph's stored importance filter.

npm install -g prism-mcp-server

prism connect prism dashboard

Use prism connect --dry-run to preview changes, prism connect --all to configure every detected host, or prism connect --refresh to reconcile Prism-managed entries after an upgrade. Restart the host after connecting.

prism dashboard opens the current local Mind Palace without a Synalux login. Prism works locally without an account, API key, or cloud subscription. Add a Synalux subscription when you want cloud memory, paid-tier skills, or team features.

To check your plan, open ⚙ Account & Settings → Account in the dashboard. Free users see Start 14-day trial; paid users see Manage subscription. During a trial, the same panel shows the paid tier and exact end date. No card is required to start. Add payment details before the end date to continue; if you do not, the subscription cancels and local Prism Free remains available. You can also compare plans directly at synalux.ai/pricing#prism-plans.

After a few sessions, ask what it's been worth:

prism savings --period month
💾 Local serving — LAST 30 DAYS

~510K tokens kept off your cloud model 53 call(s) served locally of 58 routed (91%)

Your numbers will differ — that's the point: it reports what *your* machine actually served, not a projection. Full report anatomy and the honesty rules behind it are in the local_savings section.

Install as a plugin

Prism also ships as a plugin, which registers the MCP server and the startup skill for you.

Both hosts install straight from this repository. There is nothing to host and no server to run: the catalogue is the .claude-plugin/marketplace.json file committed here, and your client clones it from GitHub.

Claude Code:

claude plugin marketplace add dcostenco/prism-coder

claude plugin install prism-coder@prism

Codex:

codex plugin marketplace add dcostenco/prism-coder

codex plugin add prism-coder@prism

The plugin registers prism-mcp via npx -y prism-mcp-server. If you already configured Prism by hand — prism connect writes an mcp_servers.prism-mcp entry — you have that server twice under one key. Install the plugin or run prism connect, not both.

What prism connect changes about host subagents

connect steers bounded work to prism_infer on your machine rather than to host-spawned agents. What it writes differs per host, and it does not disable subagents everywhere — Claude Code keeps them and is pointed at an economy model instead. Prism's local workers stay available over MCP in every case.

HostSetting writtenEffect
Claude Codeenv.CLAUDE_CODE_SUBAGENT_MODEL = "sonnet" in ~/.claude/settings.jsonSubagents stay enabled, pinned to an economy model. Fan-out is discouraged by policy text, not by config

| Gemin

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