Graphify MCP — 使用場景, 安裝 & 即時演示

為什麼要用

核心特性

Multimodal input: code (25 languages via tree-sitter), PDF, Markdown, images, video, audio
Output: interactive HTML graph, graph.json, and Markdown report
Leiden community detection groups related concepts automatically
Slash commands: /graphify query, /graphify path, /graphify explain
Hooks integration so the graph is referenced automatically, not just on demand
--watch mode: auto-update on file changes; merge-graphs for cross-repo views

即時演示

實際使用效果

graphify-skill.replay ▶ 就緒

0/0

安裝

選擇你的客戶端

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "graphify-skill": {
      "command": "uv",
      "args": [
        "tool",
        "install",
        "graphifyy"
      ],
      "_inferred": true
    }
  }
}

開啟 Claude Desktop → Settings → Developer → Edit Config。儲存後重啟應用。

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "graphify-skill": {
      "command": "uv",
      "args": [
        "tool",
        "install",
        "graphifyy"
      ],
      "_inferred": true
    }
  }
}

Cursor 使用與 Claude Desktop 相同的 mcpServers 格式。專案級設定優先於全域。

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "graphify-skill": {
      "command": "uv",
      "args": [
        "tool",
        "install",
        "graphifyy"
      ],
      "_inferred": true
    }
  }
}

點擊 Cline 側欄中的 MCP Servers 圖示，然後選 "Edit Configuration"。

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "graphify-skill": {
      "command": "uv",
      "args": [
        "tool",
        "install",
        "graphifyy"
      ],
      "_inferred": true
    }
  }
}

格式與 Claude Desktop 相同。重啟 Windsurf 生效。

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "graphify-skill",
      "command": "uv",
      "args": [
        "tool",
        "install",
        "graphifyy"
      ]
    }
  ]
}

Continue 使用伺服器物件陣列，而非映射。

~/.config/zed/settings.json

{
  "context_servers": {
    "graphify-skill": {
      "command": {
        "path": "uv",
        "args": [
          "tool",
          "install",
          "graphifyy"
        ]
      }
    }
  }
}

加入 context_servers。Zed 儲存後熱重載。

claude mcp add graphify-skill -- uv tool install graphifyy

一行命令搞定。用 claude mcp list 驗證，claude mcp remove 移除。

使用場景

實戰用法： Graphify

Onboard to a 5-year-old legacy codebase in hours, not weeks

👤 New hires, contractors inheriting code ⏱ ~60 min intermediate

何時使用： You've joined a project with 10k+ files, dozens of internal services, and tribal knowledge spread across PRs and Confluence.

前置條件

uv installed — curl -LsSf https://astral.sh/uv/install.sh | sh
Graphify installed globally — uv tool install graphifyy && graphify install

步驟

Index the repo

/graphify index /workspace/monolith --include '**/*.{ts,py,md}'✓ 已複製

→ Graph builds; report tells you cluster count and dominant concepts
Ask the graph

/graphify query 'how does payment processing connect to audit logging?'✓ 已複製

→ Path through concepts with file references — not just a grep
Open the visual

/graphify explain 'user-account cluster' and open the HTML viewer✓ 已複製

→ Interactive graph opens; cluster is highlighted with its bridges

結果： You know the conceptual shape of the codebase — where the hot paths live, which clusters are isolated, what depends on what.

注意事項

Graph becomes stale as the repo changes — graphify --watch to auto-update; or re-index before a big review session
Noise from vendored deps — Configure --exclude 'node_modules/,vendor/' on initial indexing

搭配使用： filesystem · context7

Build a research graph over 200 papers and ask cross-paper questions

👤 Researchers, policy analysts, lit-review writers ⏱ ~90 min intermediate

何時使用： You have a folder of 200 PDFs and need to find cross-paper threads no single paper exposes.

步驟

Index the corpus

/graphify index /research/climate-pdfs✓ 已複製

→ Concept clusters emerge (e.g. 'carbon pricing', 'adaptation finance')
Bridge-finding query

/graphify path 'adaptation finance' → 'sovereign debt'✓ 已複製

→ Ordered chain of concepts + papers that connect the two
Generate a literature review

Summarize the 'adaptation finance → sovereign debt' bridge as a 400-word section with paper citations.✓ 已複製

→ Draft paragraph with correct citations from the graph

結果： Insights that come from the structure of the corpus, not just keyword grep.

注意事項

Scanned PDFs don't OCR — Run OCR (ocrmypdf) before indexing; Graphify won't magic-text images

搭配使用： filesystem

Merge graphs from multiple repos into one searchable view

👤 Platform teams owning microservice fleets ⏱ ~120 min advanced

何時使用： You run 15 services. You want one graph that spans them so an agent can follow a request end-to-end.

步驟

Index each service

For each repo under /services/, run graphify index with --out graph-<name>.json✓ 已複製

→ 15 graph.json files on disk
Merge

graphify merge-graphs graph-*.json --out fleet.json✓ 已複製

→ One unified graph with cross-repo edges
Ask end-to-end

/graphify query 'trace a POST /checkout through the fleet'✓ 已複製

→ Path through multiple services, each node annotated with its repo

結果： Cross-service traceability without a service mesh or OTEL rollout.

注意事項

Same concept named differently across repos — Graphify's similarity merge handles most; for the rest, define aliases in the config

搭配使用： filesystem · github

組合

與其他 MCP 搭配，撬動十倍槓桿

graphify-skill + filesystem

Graphify points at files; filesystem lets the agent actually read/edit them

/graphify query 'retry logic' → list files, then filesystem read_text_file each one to draft a refactor.✓ 已複製

graphify-skill + github

Turn graph queries into PR plans

Use the graph to find everywhere we do ad-hoc retries; open a GitHub issue listing each file with a proposed consolidation.✓ 已複製

graphify-skill + sequential-thinking

Multi-step reasoning that uses the graph as an oracle

Plan the refactor step-by-step; at each step, consult /graphify query for impact.✓ 已複製

工具

此 MCP 暴露的能力

工具	輸入參數	何時呼叫	成本
/graphify index	path, include, exclude, --watch	First — build the graph	Claude calls for concept extraction (use prompt caching)
/graphify query	natural-language question	The primary query interface	free (local) + small model call
/graphify path	concept A → concept B	When you want to see how two ideas connect	free
/graphify explain	node/cluster id	Deep-dive on a single cluster	free + small model call
graphify merge-graphs	graph-*.json	Cross-repo / cross-folder views	free

成本與限制

運行它的成本

API 配額: Local indexing is free; concept extraction uses Claude API (your key)
每次呼叫 Token 數: Indexing a 10k-file repo ≈ tens of dollars with caching; queries are cheap
費用: Free software; pay for the extraction calls only
提示: Cache aggressively — Graphify already does prompt caching for extraction; index once, query 1000x

安全

權限、密鑰、影響範圍

憑證儲存： Uses your Claude API key for extraction; graph.json stored locally

資料出站： Concept extraction calls go to Anthropic (your account). No telemetry from Graphify itself.

切勿授予： Sharing graph.json publicly when indexed from a private repo — it embeds code snippets and filenames

graph.json can contain sensitive snippets. .gitignore it unless you want the graph shared with the repo.
--watch spawns file-system watchers; on huge repos, set include/exclude to avoid pegging a CPU.

故障排查

常見錯誤與修復

graphify: command not found after install

uv tool install graphifyy puts the binary in ~/.local/bin; ensure that's on PATH, or run with uv tool run graphify.

驗證： uv tool list | grep graphify

Indexing takes forever on a large repo

Scope with --include '**/*.{py,ts,md}' and --exclude 'node_modules/,dist/'. Concept extraction, not parsing, is the bottleneck.

驗證： Watch the progress log; parsing should be seconds, extraction takes the time

Queries return irrelevant clusters

Graph quality depends on the index — add README/docs files to the input set so Claude has project context for naming clusters

驗證： Rebuild including /docs/**/*.md; queries should sharpen

Merge-graphs produces duplicate nodes

Lower --similarity-threshold (default ~0.8) to 0.7 to merge near-duplicates more aggressively; tune per-repo

替代方案

Graphify 對比其他方案

替代方案	何時用它替代	權衡
Serena	You want LSP-precise symbol navigation and refactor tools, not conceptual clusters	No multi-modal input; no clustering
Memory MCP	You want Claude to remember session facts, not index a corpus	No graph; no query-by-concept
Raw embedding + vector DB	You need custom retrieval logic and don't care about community detection	You build the whole pipeline — graphify is opinionated and faster to ship

Graphify

為什麼要用

核心特性

即時演示

實際使用效果

安裝

選擇你的客戶端

使用場景

實戰用法： Graphify

Onboard to a 5-year-old legacy codebase in hours, not weeks

前置條件

步驟

注意事項

Build a research graph over 200 papers and ask cross-paper questions

步驟

注意事項

Merge graphs from multiple repos into one searchable view

步驟

注意事項

組合

與其他 MCP 搭配，撬動十倍槓桿

工具

此 MCP 暴露的能力

成本與限制

運行它的成本

安全

權限、密鑰、影響範圍

故障排查

常見錯誤與修復

替代方案

Graphify 對比其他方案

更多

資源