Kimi Code

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Delegate codebase analysis from Claude to Kimi Code (kimi-for-coding, 256K) — cut Claude-side token cost ~90%.

Task	Claude only	Claude + kimi-code-mcp	Claude-side savings
Analyze 200-file monorepo	~250K tok	~25K tok	~90%
Summarize 50-page RFC PDF	~60K tok	~6K tok	~90% (sketch)
Cross-reference 100 commits	~80K tok	~8K tok	~90% (sketch)

_{*Illustrative estimates — savings are on Claude tokens only and depend on the task; Kimi's subscription cost is separate. See Token Economics.}

Quick start

# 1. Install Kimi CLI and log in
curl -L code.kimi.com/install.sh | bash
kimi login

# 2. Install via npm
npm install -g kimi-mcp-server

Add to .mcp.json (project-level or ~/.claude/mcp.json for global):

{
  "mcpServers": {
    "kimi-code": {
      "command": "npx",
      "args": ["-y", "kimi-mcp-server"]
    }
  }
}

Run /mcp in Claude Code to verify — you should see kimi-code with 8 tools.

[!TIP] You don't need the CLI for the common case. kimi_query and kimi_verify call the Kimi Code API directly — no Python CLI install or kimi login required. Just provide an API key via $KIMICODE_API_KEY or ~/.kimi/config.toml (see Kimi Code API Setup). Only the codebase-reading tools (kimi_analyze, kimi_resume) need the CLI. See Two backends: API vs CLI for the full split.

How it works

1. Claude calls the kimi_analyze tool when a task needs bulk codebase reading.
2. MCP routes the request to Kimi Code (kimi-for-coding, 256K context) — Kimi reads the entire codebase in one pass.
3. The result is piped back as a structured response — Claude acts on it with precise, targeted edits.

┌──────────────┐  stdio/MCP   ┌──────────────┐  subprocess   ┌──────────────┐
│  Claude Code │ ◄──────────► │ kimi-code-mcp│ ────────────► │ Kimi CLI     │
│  (conductor) │              │ (MCP server) │               │ (256K ctx)   │
└──────────────┘              └──────────────┘               └──────────────┘

Two backends: API vs CLI

The server reaches Kimi two different ways, and each tool uses the one that fits its job. Knowing which is which tells you what you need to set up.

Backend	How it talks to Kimi	What it needs	Sees your codebase?
Direct API	HTTPS to api.kimi.com/coding/v1	An API key only ($KIMICODE_API_KEY or ~/.kimi/config.toml)	❌ No — you paste in the context
Local CLI	Spawns the kimi binary as a subprocess	CLI installed and kimi login done	✅ Yes — reads files from disk

Tool	Backend	Why
kimi_query	API (CLI only if no key configured)	Contextless Q&A — no codebase needed, so the API is simpler and has no login dependency
kimi_verify	API	You pass the code/diff/claim inline; Kimi judges it as an independent third party
kimi_analyze	CLI	Must read your whole codebase (256K ctx) from disk
kimi_resume	CLI	Continues a stateful CLI session that holds prior codebase context
kimi_list_sessions, kimi_cache_*, kimi_status	local	Read local session/cache metadata

[!IMPORTANT] Most users only need the API key. If you just want a second opinion / verification (kimi_query, kimi_verify), set the API key and you're done — skip the CLI entirely. Install + kimi login only when you want Kimi to read your codebase via kimi_analyze / kimi_resume.

Run kimi_status any time to see which backends are live — it reports the API-configured state and the CLI install/auth state separately.

Guidelines for agents

If you are an AI agent (Claude Code, a subagent, etc.) deciding when to call these tools:

• Cross-check your own work before committing → kimi_verify. Paste the actual diff/code/claim plus the surrounding context (goal, constraints, signatures). Kimi sees only the context string — no repo, no session history. Vague context → useless review.
• Quick model-agnostic programming question → kimi_query. No codebase needed. Returns a different model's opinion.
• Need to understand a large/unfamiliar codebase → kimi_analyze with work_dir. Prefer this over reading 50 files yourself; it's ~10× cheaper in Claude tokens. Requires the CLI to be installed and logged in.
• Drill deeper after an analyze → kimi_resume with the returned session_id (retains up to 256K tokens of prior context).
• Don't know why a Kimi call failed → kimi_status first. "Not authenticated" on the CLI does not affect kimi_query/kimi_verify (those use the API).
• Keep outputs lean. Default detail_level: summary for orientation; raise to normal/detailed only when you need code snippets. Bigger output = more Claude tokens, defeating the purpose.
• Skip Kimi for small/single-file work — Claude reading directly is faster under ~10 files.

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MCP server that connects Kimi Code (model kimi-for-coding, 256K context, auto-upgraded) with Claude Code — letting Claude orchestrate while Kimi handles the heavy reading.

[!TIP] Stop paying Claude to read files. Kimi Code delivers frontier-class code intelligence at a fraction of the cost (see chart above). Delegate bulk codebase scanning to Kimi (256K context, near-zero cost) and let Claude focus on what it does best — reasoning, decisions, and precise code edits. One kimi_analyze call can replace 50+ file reads.

What is Kimi Code?

Kimi Code is an AI code agent by Moonshot AI. The model ID kimi-for-coding (1T MoE, 256K context) automatically receives backend upgrades — no version pinning required. It works across Terminal, IDE, and CLI — writing, debugging, refactoring, and analyzing code autonomously.

Key specs:

• 256K token context — reads entire codebases in one pass
• Parallel agent spawning — handles concurrent tasks
• Shell, file, and web access — full developer toolchain
• Install: curl -L code.kimi.com/install.sh | bash

[!WARNING] Kimi Code membership required. All tools ultimately hit Kimi Code, which needs an active Kimi Code plan. The API tools (kimi_query, kimi_verify) authenticate with an API key; the codebase tools (kimi_analyze, kimi_resume) additionally need the CLI installed + kimi login. See kimi.com/code for pricing tiers and quotas.

Install from source

If you prefer to build locally instead of using the npm package:

git clone https://github.com/howardpen9/kimi-code-mcp.git
cd kimi-code-mcp && npm install && npm run build

{
  "mcpServers": {
    "kimi-code": {
      "command": "node",
      "args": ["/absolute/path/to/kimi-code-mcp/dist/index.js"]
    }
  }
}

Kimi Code API Setup

[!NOTE] Kimi Code API and Moonshot API are separate providers — their API keys are not interchangeable.

There are two ways to configure the Kimi Code API for the CLI:

Option 1: OAuth Login (Recommended)

In the Kimi Code CLI shell, run:

kimi

Then use the /login (or /setup) command:

/login

1. Select Kimi Code as the platform
2. Your browser opens for OAuth authorization
3. Config is saved automatically to ~/.kimi/config.toml

[!NOTE] zsh: command not found: kimi after install? The installer puts the binary at ~/.local/bin/kimi, which may not be on your PATH. Add it (then restart your shell or open a new tab):

echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc && source ~/.zshrc

The MCP server calls the binary by absolute path, so this only affects running kimi yourself in a terminal (e.g. for kimi login).

Option 2: Manual API Key Configuration

Get your API Key

1. Visit code.kimi.com
2. Sign in → Settings → API Keys
3. Create a new key (starts with sk-, shown only once)

Edit config file

nano ~/.kimi/config.toml

Add:

[providers.kimi-code]
type = "kimi"
base_url = "https://api.kimi.com/coding/v1"
api_key = "sk-your-api-key"

[models.kimi-for-coding]
provider = "kimi-code"
model = "kimi-for-coding"
max_context_size = 262144
capabilities = ["thinking"]

[defaults]
model = "kimi-for-coding"

Using environment variables (recommended for security)

# Add to ~/.zshrc (macOS) or ~/.bashrc (Linux)
export KIMICODE_API_KEY="sk-your-api-key"

Then reference it in config.toml:

[providers.kimi-code]
type = "kimi"
base_url = "https://api.kimi.com/coding/v1"
api_key = "${KIMICODE_API_KEY}"

Multi-provider config example

You can configure both Kimi Code and Moonshot side by side:

[providers.kimi-code]
type = "kimi"
base_url = "https://api.kimi.com/coding/v1"
api_key = "${KIMICODE_API_KEY}"

[providers.moonshot-cn]
type = "kimi"
base_url = "https://api.moonshot.cn/v1"
api_key = "${MOONSHOT_API_KEY}"

[models.kimi-for-coding]
provider = "kimi-code"
model = "kimi-for-coding"
max_context_size = 262144
capabilities = ["thinking"]

[models.kimi-k2]
provider = "moonshot-cn"
model = "kimi-k2-0905-preview"
max_context_size = 256000
capabilities = ["thinking"]

[defaults]
model = "kimi-for-coding"

Switch models at any time with /model or /model kimi-k2 in the CLI.

Kimi Code vs Moonshot

Feature	Kimi Code	Moonshot
Focus	Optimized for coding	General-purpose chat
Endpoint	api.kimi.com/coding/v1	api.moonshot.cn/v1
API Key	Separate — apply at code.kimi.com	Separate
SearchWeb / FetchURL	Built-in	Not available
Context	262K	256K

What You Can Do

Just tell Claude what you need. It will delegate to Kimi automatically:

Prompt	What happens
"Analyze this codebase's architecture"	Kimi reads all files (256K ctx), Claude acts on the report
"Scan for security vulnerabilities, then review Kimi's findings"	Kimi audits, Claude cross-examines — AI pair review
"Map all dependencies of the auth module, then plan the refactoring"	Kimi builds the dependency graph, Claude plans the changes
"Review the recent changes for regressions and edge cases"	Kimi reviews full context (not just the diff), Claude synthesizes
"Resume the last Kimi session and ask about the API design"	Kimi retains 256K tokens of context across sessions

Why This Exists

Claude Code is powerful but expensive. Every file it reads costs tokens. Meanwhile, many tasks — pre-reviewing large codebases, scanning for patterns, generating audit reports — are high-certainty work that doesn't need Claude's full reasoning power.

[!IMPORTANT] The cost equation: Claude reads 50 files to understand your architecture = expensive. Kimi reads 50 files via kimi_analyze = near-zero cost. Claude then acts on Kimi's structured report = minimal tokens. Total savings: 60-80% fewer Claude tokens on analysis-heavy tasks.

How It Saves Tokens

                          ┌─────────────────────────────┐
                          │   You (the developer)       │
                          └──────────┬──────────────────┘
                                     │ prompt
                                     ▼
                          ┌─────────────────────────────┐
                          │   Claude Code (conductor)   │
                          │   - orchestrates workflow    │
                          │   - makes decisions          │
                          │   - writes & edits code      │
                          └──────┬──────────────┬───────┘
                      precise    │              │  delegate
                      edits      │              │  bulk reading
                      (tokens)   │              │  (FREE)
                                 ▼              ▼
                          ┌──────────┐   ┌──────────────┐
                          │ your     │   │  Kimi Code   │
                          │ codebase │   │  - 256K ctx  │
                          └──────────┘   │  - reads all │
                                         │  - reports   │
                                         └──────────────┘

1. Claude receives your task → decides it needs codebase understanding
2. Claude calls kimi_analyze via MCP → Kimi reads the entire codebase (256K context, near-zero cost)
3. Kimi returns a structured analysis
4. Claude acts on the analysis with precise, targeted edits

Result: Claude only spends tokens on decision-making and code writing, not on reading files.

Mutual Code Review with Kimi Code

kimi-for-coding is a 1T MoE model designed for deep code comprehension. This enables AI pair review:

1. Kimi pre-reviews — 256K context means it sees the entire codebase at once: security issues, anti-patterns, dead code, architectural problems
2. Claude cross-examines — reviews Kimi's findings, challenges questionable items, adds its own insights
3. Two perspectives — different models catch different things. What one misses, the other finds

Use Kimi as a Code Reviewer

Beyond ad-hoc analysis, you can use Kimi as a dedicated reviewer in your workflow:

PR Review Workflow

┌──────────────┐   diff    ┌──────────────┐  structured  ┌──────────────┐
│   Your PR    │ ────────► │  Kimi Code   │  findings    │  Claude Code │
│  (changes)   │           │  (reviewer)  │ ────────────►│  (decision)  │
└──────────────┘           └──────────────┘              └──────────────┘

Continuous Audit Pattern

When	What	Why
Before merging	Kimi scans diff + affected modules	Catch regressions early
Weekly	Full codebase sweep	Accumulated tech debt
Pre-release	Security-focused audit	Ship with confidence

Each review session can be resumed (kimi_resume) — Kimi retains up to 256K tokens of context from previous sessions, building understanding over time.

What Kimi Reviews Well

Review Type	Why Kimi Excels
Security audit	256K context sees full attack surface, not just isolated files
Dead code detection	Can trace imports/exports across entire codebase
API consistency	Compares patterns across all endpoints simultaneously
Dependency analysis	Maps full dependency graph in one pass
Architecture review	Sees the forest and the trees at the same time

Tools

Tool	Description	Timeout
kimi_analyze	CLI — deep codebase analysis (architecture, audit, refactoring)	10 min
kimi_query	API — quick programming questions, no codebase context (CLI only if no key configured)	2 min
kimi_verify	API — independent third-party verification of code/diffs/claims; no CLI required, context-driven	5 min
kimi_list_sessions	List existing Kimi sessions with metadata	instant
kimi_resume	CLI — resume a previous session (up to 256K token context)	10 min
kimi_status	Report API-configured state + CLI install/version/auth status	instant
kimi_cache_status	View session cache statistics and performance metrics	instant
kimi_cache_invalidate	Manually invalidate cached sessions (by dir or all)	instant

Output Control Parameters

kimi_analyze and kimi_resume support these parameters to control output size:

Parameter	Values	Default	Effect
detail_level	summary / normal / detailed	normal	Controls prompt-side verbosity instructions
max_output_tokens	number	15000	Hard ceiling — output truncated at clean boundary if exceeded
include_thinking	boolean	false	Include Kimi's internal reasoning chain (10-30K extra tokens)

kimi_query also supports max_output_tokens and include_thinking.

Token Economics

[!NOTE] The savings come from compression ratio, not from free reading. Kimi's subscription cost still applies, but the key benefit is reducing expensive Claude Code token consumption.

                    Without kimi-code-mcp        With kimi-code-mcp (normal)
                    ─────────────────────        ───────────────────────────
Raw source:         50 files × ~4K = 200K        Kimi reads (subscription cost)
Claude reads:       200K tokens                  5-15K token report
Claude token cost:  $$$                          $

Compression ratio by detail_level:

Level	Compression	Output Size	Equivalent Source	Best For
summary	40-100x	~2-5K tokens	~8-20K chars / ~200-500 lines of code	Quick orientation, file inventory
normal	15-40x	~5-15K tokens	~20-60K chars / ~500-1500 lines of code	Architecture review, dependency mapping
detailed	5-15x	~15-40K tokens	~60-160K chars / ~1500-4000 lines of code	Security audit with code snippets

When savings happen:

• Large codebases (50+ files) — architecture understanding, cross-file scanning
• Security audits, dead code detection, API consistency checks
• Pre-review before targeted edits (scan first → edit specific files)

When to skip and let Claude read directly:

• Small codebases (<10 files) — direct reading is faster
• Single-file modifications — Claude's built-in file reading is sufficient
• When you need every line of code — detailed output approaches raw reading cost

Implementation details

Under the hood:

1. Claude Code calls an MCP tool (e.g., kimi_analyze)
2. This server spawns the kimi CLI with the prompt and codebase path
3. Kimi autonomously reads files, analyzes the code (up to 256K tokens)
4. The result is parsed from Kimi's JSON output and returned to Claude Code
5. Claude acts on the structured results — edits, plans, or further analysis

CLI Invocation Reference

The MCP server calls the Kimi CLI in non-interactive (print) mode:

kimi --work-dir <path> --print -p "<prompt>"

Flag	Purpose
--print	Non-interactive mode — outputs result and exits (required for subprocess use)
-p / --prompt	Pass prompt directly (bypasses interactive shell)
--work-dir / -w	Set codebase root directory
-S <id>	Resume a specific session by ID
--no-thinking	Disable thinking mode

[!NOTE] There is no kimi analyze subcommand. The MCP tool is named kimi_analyze, but the underlying CLI uses the flags above. Use this syntax to call Kimi directly for debugging or scripting.

Advanced Setup

For development (auto-recompile on changes):

{
  "mcpServers": {
    "kimi-code": {
      "command": "npx",
      "args": ["tsx", "/absolute/path/to/kimi-code-mcp/src/index.ts"]
    }
  }
}

npm

Published as kimi-mcp-server on npm.

npx kimi-mcp-server          # run directly
npm install -g kimi-mcp-server # install globally

Project Structure

src/
├── index.ts           # MCP server setup, tool definitions, API-vs-CLI routing
├── kimi-api.ts        # Direct Kimi Code API client (kimi_query / kimi_verify)
├── kimi-runner.ts     # Spawns kimi CLI, parses output, handles timeouts
├── cache-manager.ts   # Session cache (warmup, reuse, invalidation)
└── session-reader.ts  # Reads Kimi session metadata from ~/.kimi/

Contributing

See CONTRIBUTING.md for guidelines.

Changelog

See CHANGELOG.md for version history.

License

MIT