How do I integrate cc-sensei with my AI client?

Add the server command and path to your client's MCP configuration under mcpServers, then restart the client.

Does cc-sensei require network access to function?

No, it runs fully locally with zero network dependencies.

What kinds of queries can I make with cc-sensei?

You can list modules, query architecture details, trace concerns, search design patterns, and read source code.

Is there a quick way to try cc-sensei without full install?

Yes, you can run it instantly via 'npx cc-sensei' without installing.

Cc Sensei

knowledgearchitecturesource-codemcpagent-tools

Provides a local MCP server indexing Claude Code's 310K lines into 32 modules with 6 tools for architecture queries and source lookup.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

We couldn’t automatically confirm this listing starts correctly

The install command below started, but didn't respond the way we expected when we tried to talk to it.

npx -y cc-sensei

initialize succeeded but no response to tools/list.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "contradictory-body-cc-sensei": {
      "command": "npx",
      "args": [
        "-y",
        "cc-sensei"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

This MCP server distills Claude Code's extensive source code into 32 structured architecture modules with 40K lines of analysis. It exposes six tools that let AI agents query module lists, architecture details, cross-cutting concerns, reusable design patterns, and source code snippets. It runs fully locally with sub-120ms p95 latency and zero network dependencies, ideal for agents needing deep architectural insight into Claude Code without parsing the raw source.

Use cases

Query Claude Code architecture modules by natural language

Trace design concerns across multiple modules

Retrieve reusable design patterns for agent development

Access precise source code snippets with line references

List all core modules and their responsibilities

Key features

32 queryable architecture modules from Claude Code

6 specialized tools for architecture and source queries

Natural language search with adjustable depth

Cross-trace concerns across all modules

Source code reading with line numbers and ranges

Fully local operation with sub-120ms p95 latency

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Cc Sensei.

Extracted Tool Capabilities

32 queryable architecture modules from Claude Code

6 specialized tools for architecture and source queries

Natural language search with adjustable depth

Cross-trace concerns across all modules

Source code reading with line numbers and ranges

Fully local operation with sub-120ms p95 latency

Documentation Overview

🎓 cc-sensei

Inject every ounce of Claude Code's architectural wisdom into your AI's brain

English | 中文

cc-sensei: 310K lines of source distilled into 32 queryable modules via MCP

Sound familiar?

😩 "I've already dug deep into Claude Code's source, but when I ask my AI to help me build an Agent, it has no idea what I'm talking about — what I read, it didn't."

😫 "I'd love to build a knowledge base so my AI can learn from Claude Code, but the moment I open it — 300K+ lines, 1,250 files — both my AI and I are scared off."

😵 "I've barely looked at Claude Code's source, but I still want an Agent just as powerful — where do I even begin?"

If any of these hit home — this project is built for you.

What it does for you

Agent Architecture Oracle distills Claude Code's 310K lines of source and 1,250 files into 32 core modules with 40K lines of structured analysis, and serves it directly to your AI through the MCP protocol.

It covers every core capability of Claude Code:

🔁 Agent main loop · 🛠️ Tool system & execution pipeline · 🛡️ Permissions & security sandbox · 🌊 Model API & streaming 🧠 Context engineering & compaction · 📂 File / Shell / Git tools · 🔌 MCP protocol · 🎨 Ink rendering engine · 🧩 Skills/plugins 💾 Persistent memory · ⚡ Prompt cache break detection · 🤖 Sub-agents & task system … (22 core + 10 deep-dive supplements)

It establishes a complete index pipeline: natural language → module → design decision → reusable pattern → corresponding source code:

Code

       You say one thing                  Oracle automatically does five things
  ─────────────────────────         ──────────────────────────────────────────
  "How do you compact long       ──►  ① Match natural language to M06 (Context Engineering)
   conversations?"                     ② Return the 4-tier compaction architecture & 9-section summary prompt
                                       ③ List 5 directly-copyable engineering principles
                                       ④ Cross-trace the 12 modules touched by prompt cache
                                       ⑤ Pull up services/compact/microCompact.ts source on demand

After plugging it into Claude Desktop / Cursor / Qoder / your own Agent, your AI gains a "Chief Architect of Claude Code" as its consultant — whether you're cloning the whole thing or just stealing one design, it tells you exactly where to look, why it works, and how to copy it.

In one night, your AI becomes a world-class Agent architect.

🚀 Quick Start (3 steps)

Option A — Try instantly via npx (zero install)

Terminal

npx cc-sensei

This downloads and runs the server directly. Skip to Step 3 to connect your AI.

Option B — Clone for full control

Step 1 — Clone the project

bash

git clone https://github.com/contradictory-body/cc-sensei.git && cd cc-sensei

Step 2 — Install and build

Copy-paste again:

bash

pnpm install && pnpm build

💡 Don't have pnpm? Run npm install -g pnpm first. 💡 This step parses 32 module analyses, generates the knowledge index, and compiles the server. One-time only.

When done, you should see: ✅ module-registry.json (32 modules) and ✅ Build success.

Step 3 — Connect your AI

Add this snippet to your MCP client's configuration file (replace the path with the one you just cloned to):

config.json

{
  "mcpServers": {
    "cc-sensei": {
      "command": "node",
      "args": ["/your/absolute/path/cc-sensei/dist/server.js"]
    }
  }
}

Don't know where the config file is? Common locations:

Client	Config file path
Claude Desktop (macOS)	`~/Library/Application Support/Claude/claude_desktop_config.json`
Claude Desktop (Windows)	`%APPDATA%\Claude\claude_desktop_config.json`
Cursor	Settings → MCP → Add new server
Qoder	Settings → MCP → Edit mcpServers

Restart your client. Done! Your AI just gained 6 new skills.

🎯 Try it out

Just say to your AI:

"Use cc-sensei to tell me how Claude Code optimizes prompt cache."

It will call the tools on its own and return a complete analysis with source-code references. That's it.

Dive deeper

🛠️ What the 6 tools do

Tool	One-line capability	When to use
`list_modules`	List all 32 modules and their concerns	"Which modules does Claude Code break down into?"
`query_architecture`	Natural-language search with 3 depth levels (brief/standard/deep)	"How do they prevent long-conversation context overflow?"
`get_module`	Drill into one module's specific section (responsibility / architecture / decisions / principles / relations)	"Show me M06's design principles"
`trace_concern`	Trace one concern across all 32 modules, ranked by hit count	"Which modules touch 'prompt cache'?"
`search_patterns`	Bulk-extract reusable patterns with built-in size limiting	"Give me every cache-related design I can copy"
`get_source_code`	Read Claude Code source directly (with line numbers, ranges, and directory listings)	"Show me `services/api/claude.ts:1412-1456`"

Every tool comes with:

✅ Path-prefix tolerance — both src/... and bare paths are accepted; no more copy-paste failures
✅ Result rate-limiting — 800 chars per section and 12 sections by default, so your AI's context window stays alive
✅ Path-traversal protection — attacks like ../../../etc/passwd are rejected outright
✅ Graceful degradation — bad arguments return friendly hints instead of crashes

🎬 Real scenario: "I want to add context compaction to my Agent"

Code

You:    "How do you compact conversation history to avoid hitting the token limit?"

Oracle (query_architecture, 103ms):
  → Hit M06: Context Engineering
  → Returns the 4-tier compaction architecture comparison table
    (microCompact / sessionMemoryCompact / autoCompact / reactiveCompact)
  → Trigger conditions, whether LLM is invoked, key file locations — all listed

You:    "Show me M06's key design principles"

Oracle (get_module section=principles, 102ms):
  → "Sticky-on Beta Headers — performance over simplicity"
    "Single message-level cache_control marker (max 4)"
    "9-section summary structure — eval-driven prompt"
  → Each principle includes: where it lives, code snippet, why, how to reuse, the trade-off

You:    "How many modules does 'prompt cache' span?"

Oracle (trace_concern, 118ms):
  → 12 modules ranked by hit count
  → Each with 5 lines of file:line excerpts

You:    "Show me src/services/compact/microCompact.ts"

Oracle (get_source_code, 99ms):
  → Source with line numbers + total line count

A complete "understand → copy → ship" loop, averaging ~110ms.

📦 Knowledge scale

Code

📚 32 module analyses  (40K lines of distilled insight covering 310K lines of source)
├── M01-M22  Core modules
│   M01 Process bootstrap & lifecycle · M02 Agent main loop · M03 Tool system
│   M04 Permissions & security · M05 Model API & streaming · M06 Context engineering
│   M07 File/Shell/Git tools · M08 MCP protocol · M09 LSP integration
│   M10 Bridge/IPC · M11 Ink rendering engine · M12 Message rendering
│   M13 Input system · M14 Sub-agents & tasks · M15 Skills/plugins
│   M16 Command system · M17 Configuration & settings · M18 Telemetry & analytics
│   M19 State management · M20 Hotkeys & focus · M21 Buddy/voice · M22 Testability
└── SUPP-* 10 deep-dive supplements
    AgentSummary · SessionMemory · autoDream · ColorDiff
    extractMemories · fileIndex · largeFiles · speculation
    teamMemorySync · yogaLayout

🗂️ Indexes: 390 keywords / 104 concerns / full section-line mappings
💻 Full source code: claude-code-main/src/  available for direct local reads

🏗️ Engineering philosophy

This project is dogfooding itself — built using the methods Claude Code teaches, made to serve Claude.

Claude Code's design philosophy	How this project applies it
Build-time indexing + zero runtime analysis	Section line ranges, keywords, and concerns are all computed in `pnpm build:index`; runtime just looks up JSON
Path-traversal gatekeeping	`validatePath` runs before every file read
Graceful degradation > unhandled crashes	Section not found? Return the "available types" hint
Structured prompt-as-spec	All 32 MODULE_NOTES strictly follow a 10-section template
Rate-limiting by default	search_patterns defaults to 800 chars per section, 12 sections cap, configurable

⚡ Performance

Code

p50 = 105ms   p95 = 118ms   max = 125ms
Average response size: 7K chars
Fully local, 0 network dependency

✅ Tests

bash

pnpm exec tsx scripts/test-suite.ts          # 30 baseline functional tests
pnpm exec tsx scripts/regression-3fixes.ts   # 11 UX-fix regression tests
pnpm exec tsx scripts/e2e-comprehensive.ts   # 24 real-scenario E2E tests

Test suite	Cases	Result
Baseline functionality (6 tools × multiple branches)	30	✅ 30/30
UX-fix regression	11	✅ 11/11
Real user-scenario E2E (3 personas)	24	✅ 24/24
Total	65	✅ 65/65

📁 Project structure

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

npm downloads

Package downloads in the last 30 days.

Last commit

2mo ago

Most recent push to the default branch.

Install check

Inconclusive

Started but did not complete the MCP handshake in our test.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Cc Sensei

It distills Claude Code's large source into structured modules and serves them via MCP for AI agents to query architecture and source.

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[![AllMCPs](https://allmcps.com/api/badge/contradictory-body-cc-sensei?style=directory)](https://allmcps.com/mcp/contradictory-body-cc-sensei)

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Technical Specs & Signals

CategoryKnowledge & Memory

PricingFree

10/10 checks healthy over the last 32d

52Quality signal: Good · 52/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools24/30

Adoption & activity5/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 23d ago via OSV.dev · cc-sensei (npm)

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MCP & Agent Skills for Automated Documentation, and codebase conventions + context

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Explore more

More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Cc Sensei →Install in Claude Desktop Install in Cursor Install in VS Code

Cc Sensei

knowledgearchitecturesource-codemcpagent-tools

Provides a local MCP server indexing Claude Code's 310K lines into 32 modules with 6 tools for architecture queries and source lookup.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

We couldn’t automatically confirm this listing starts correctly

The install command below started, but didn't respond the way we expected when we tried to talk to it.

npx -y cc-sensei

initialize succeeded but no response to tools/list.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "contradictory-body-cc-sensei": {
      "command": "npx",
      "args": [
        "-y",
        "cc-sensei"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

Use cases

Query Claude Code architecture modules by natural language

Trace design concerns across multiple modules

Retrieve reusable design patterns for agent development

Access precise source code snippets with line references

List all core modules and their responsibilities

Key features

32 queryable architecture modules from Claude Code

6 specialized tools for architecture and source queries

Natural language search with adjustable depth

Cross-trace concerns across all modules

Source code reading with line numbers and ranges

Fully local operation with sub-120ms p95 latency

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Cc Sensei.

Extracted Tool Capabilities

32 queryable architecture modules from Claude Code

6 specialized tools for architecture and source queries

Natural language search with adjustable depth

Cross-trace concerns across all modules

Source code reading with line numbers and ranges

Fully local operation with sub-120ms p95 latency

Documentation Overview

🎓 cc-sensei

Inject every ounce of Claude Code's architectural wisdom into your AI's brain

English | 中文

Sound familiar?

😩 "I've already dug deep into Claude Code's source, but when I ask my AI to help me build an Agent, it has no idea what I'm talking about — what I read, it didn't."

😫 "I'd love to build a knowledge base so my AI can learn from Claude Code, but the moment I open it — 300K+ lines, 1,250 files — both my AI and I are scared off."

😵 "I've barely looked at Claude Code's source, but I still want an Agent just as powerful — where do I even begin?"

If any of these hit home — this project is built for you.

What it does for you

It covers every core capability of Claude Code:

🔁 Agent main loop · 🛠️ Tool system & execution pipeline · 🛡️ Permissions & security sandbox · 🌊 Model API & streaming 🧠 Context engineering & compaction · 📂 File / Shell / Git tools · 🔌 MCP protocol · 🎨 Ink rendering engine · 🧩 Skills/plugins 💾 Persistent memory · ⚡ Prompt cache break detection · 🤖 Sub-agents & task system … (22 core + 10 deep-dive supplements)

It establishes a complete index pipeline: natural language → module → design decision → reusable pattern → corresponding source code:

Code

       You say one thing                  Oracle automatically does five things
  ─────────────────────────         ──────────────────────────────────────────
  "How do you compact long       ──►  ① Match natural language to M06 (Context Engineering)
   conversations?"                     ② Return the 4-tier compaction architecture & 9-section summary prompt
                                       ③ List 5 directly-copyable engineering principles
                                       ④ Cross-trace the 12 modules touched by prompt cache
                                       ⑤ Pull up services/compact/microCompact.ts source on demand

In one night, your AI becomes a world-class Agent architect.

🚀 Quick Start (3 steps)

Option A — Try instantly via npx (zero install)

Terminal

npx cc-sensei

This downloads and runs the server directly. Skip to Step 3 to connect your AI.

Option B — Clone for full control

Step 1 — Clone the project

bash

git clone https://github.com/contradictory-body/cc-sensei.git && cd cc-sensei

Step 2 — Install and build

Copy-paste again:

bash

pnpm install && pnpm build

💡 Don't have pnpm? Run npm install -g pnpm first. 💡 This step parses 32 module analyses, generates the knowledge index, and compiles the server. One-time only.

When done, you should see: ✅ module-registry.json (32 modules) and ✅ Build success.

Step 3 — Connect your AI

Add this snippet to your MCP client's configuration file (replace the path with the one you just cloned to):

config.json

{
  "mcpServers": {
    "cc-sensei": {
      "command": "node",
      "args": ["/your/absolute/path/cc-sensei/dist/server.js"]
    }
  }
}

Don't know where the config file is? Common locations:

Client	Config file path
Claude Desktop (macOS)	`~/Library/Application Support/Claude/claude_desktop_config.json`
Claude Desktop (Windows)	`%APPDATA%\Claude\claude_desktop_config.json`
Cursor	Settings → MCP → Add new server
Qoder	Settings → MCP → Edit mcpServers

Restart your client. Done! Your AI just gained 6 new skills.

🎯 Try it out

Just say to your AI:

"Use cc-sensei to tell me how Claude Code optimizes prompt cache."

It will call the tools on its own and return a complete analysis with source-code references. That's it.

Dive deeper

🛠️ What the 6 tools do

Tool	One-line capability	When to use
`list_modules`	List all 32 modules and their concerns	"Which modules does Claude Code break down into?"
`query_architecture`	Natural-language search with 3 depth levels (brief/standard/deep)	"How do they prevent long-conversation context overflow?"
`get_module`	Drill into one module's specific section (responsibility / architecture / decisions / principles / relations)	"Show me M06's design principles"
`trace_concern`	Trace one concern across all 32 modules, ranked by hit count	"Which modules touch 'prompt cache'?"
`search_patterns`	Bulk-extract reusable patterns with built-in size limiting	"Give me every cache-related design I can copy"
`get_source_code`	Read Claude Code source directly (with line numbers, ranges, and directory listings)	"Show me `services/api/claude.ts:1412-1456`"

Every tool comes with:

✅ Path-prefix tolerance — both src/... and bare paths are accepted; no more copy-paste failures
✅ Result rate-limiting — 800 chars per section and 12 sections by default, so your AI's context window stays alive
✅ Path-traversal protection — attacks like ../../../etc/passwd are rejected outright
✅ Graceful degradation — bad arguments return friendly hints instead of crashes

🎬 Real scenario: "I want to add context compaction to my Agent"

Code

You:    "How do you compact conversation history to avoid hitting the token limit?"

Oracle (query_architecture, 103ms):
  → Hit M06: Context Engineering
  → Returns the 4-tier compaction architecture comparison table
    (microCompact / sessionMemoryCompact / autoCompact / reactiveCompact)
  → Trigger conditions, whether LLM is invoked, key file locations — all listed

You:    "Show me M06's key design principles"

Oracle (get_module section=principles, 102ms):
  → "Sticky-on Beta Headers — performance over simplicity"
    "Single message-level cache_control marker (max 4)"
    "9-section summary structure — eval-driven prompt"
  → Each principle includes: where it lives, code snippet, why, how to reuse, the trade-off

You:    "How many modules does 'prompt cache' span?"

Oracle (trace_concern, 118ms):
  → 12 modules ranked by hit count
  → Each with 5 lines of file:line excerpts

You:    "Show me src/services/compact/microCompact.ts"

Oracle (get_source_code, 99ms):
  → Source with line numbers + total line count

A complete "understand → copy → ship" loop, averaging ~110ms.

📦 Knowledge scale

Code

📚 32 module analyses  (40K lines of distilled insight covering 310K lines of source)
├── M01-M22  Core modules
│   M01 Process bootstrap & lifecycle · M02 Agent main loop · M03 Tool system
│   M04 Permissions & security · M05 Model API & streaming · M06 Context engineering
│   M07 File/Shell/Git tools · M08 MCP protocol · M09 LSP integration
│   M10 Bridge/IPC · M11 Ink rendering engine · M12 Message rendering
│   M13 Input system · M14 Sub-agents & tasks · M15 Skills/plugins
│   M16 Command system · M17 Configuration & settings · M18 Telemetry & analytics
│   M19 State management · M20 Hotkeys & focus · M21 Buddy/voice · M22 Testability
└── SUPP-* 10 deep-dive supplements
    AgentSummary · SessionMemory · autoDream · ColorDiff
    extractMemories · fileIndex · largeFiles · speculation
    teamMemorySync · yogaLayout

🗂️ Indexes: 390 keywords / 104 concerns / full section-line mappings
💻 Full source code: claude-code-main/src/  available for direct local reads

🏗️ Engineering philosophy

This project is dogfooding itself — built using the methods Claude Code teaches, made to serve Claude.

Claude Code's design philosophy	How this project applies it
Build-time indexing + zero runtime analysis	Section line ranges, keywords, and concerns are all computed in `pnpm build:index`; runtime just looks up JSON
Path-traversal gatekeeping	`validatePath` runs before every file read
Graceful degradation > unhandled crashes	Section not found? Return the "available types" hint
Structured prompt-as-spec	All 32 MODULE_NOTES strictly follow a 10-section template
Rate-limiting by default	search_patterns defaults to 800 chars per section, 12 sections cap, configurable

⚡ Performance

Code

p50 = 105ms   p95 = 118ms   max = 125ms
Average response size: 7K chars
Fully local, 0 network dependency

✅ Tests

bash

pnpm exec tsx scripts/test-suite.ts          # 30 baseline functional tests
pnpm exec tsx scripts/regression-3fixes.ts   # 11 UX-fix regression tests
pnpm exec tsx scripts/e2e-comprehensive.ts   # 24 real-scenario E2E tests

Test suite	Cases	Result
Baseline functionality (6 tools × multiple branches)	30	✅ 30/30
UX-fix regression	11	✅ 11/11
Real user-scenario E2E (3 personas)	24	✅ 24/24
Total	65	✅ 65/65

📁 Project structure

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

npm downloads

Package downloads in the last 30 days.

Last commit

2mo ago

Most recent push to the default branch.

Install check

Inconclusive

Started but did not complete the MCP handshake in our test.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Cc Sensei

It distills Claude Code's large source into structured modules and serves them via MCP for AI agents to query architecture and source.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/contradictory-body-cc-sensei?style=directory)](https://allmcps.com/mcp/contradictory-body-cc-sensei)

HTML Embed

<a href="https://allmcps.com/mcp/contradictory-body-cc-sensei"><img src="https://allmcps.com/api/badge/contradictory-body-cc-sensei?style=directory" alt="Cc Sensei on AllMCPs" /></a>

Technical Specs & Signals

CategoryKnowledge & Memory

PricingFree

10/10 checks healthy over the last 32d

52Quality signal: Good · 52/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools24/30

Adoption & activity5/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 23d ago via OSV.dev · cc-sensei (npm)

★ Featured

Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Cc Sensei →Install in Claude Desktop Install in Cursor Install in VS Code