Kimi K3 MCP

A small, read-only Model Context Protocol server that gives Codex and other MCP clients a Kimi K3 second opinion through OpenRouter.

It exposes three tools:

• review_algorithm: challenge correctness, invariants, edge cases, counterexamples, and complexity.

• review_code: find concrete correctness, security, reliability, and performance defects.

• plan_project: produce an implementation-ready plan with dependencies, verification, risks, and rollback.

The server cannot read or modify local files. The MCP client chooses what code and context to send. Submitted content is treated as untrusted data, and Kimi's private reasoning is excluded from tool results. Responses include token usage, cost, latency, provider, and retry metadata.

The tools are strictly opt-in. Server instructions tell the MCP client not to call them for routine reviews or planning; explicitly mention Kimi, K3, the Kimi MCP, or a tool name when you want a call.

Requirements

• Python 3.11 or newer

• uv

• An OPENROUTER_API_KEY with access to moonshotai/kimi-k3

Related MCP server: mcp-agent-review

Install

git clone git@github.com:catid/k3mcp.git
cd k3mcp
uv sync --frozen

Do not put the OpenRouter key in this repository or in config.toml. Export it in the environment that starts Codex:

export OPENROUTER_API_KEY=sk-or-v1-...

Configure Codex

Add the server to ~/.codex/config.toml. Adjust the absolute path to the clone:

[mcp_servers.kimi_k3]
command = "/absolute/path/to/k3mcp/.venv/bin/k3mcp"
env_vars = ["OPENROUTER_API_KEY"]
enabled = true
required = false
startup_timeout_sec = 20
tool_timeout_sec = 15600
default_tools_approval_mode = "approve"

Restart Codex after changing its MCP configuration. In the TUI, /mcp shows whether the server and its tools loaded successfully.

Other MCP clients

The server uses stdio transport:

OPENROUTER_API_KEY=... uv run k3mcp

The process writes MCP protocol messages to stdout. Application logs must go to stderr.

Configuration

Kimi K3 reasoning is mandatory on OpenRouter. The server requests maximum effort and excludes the reasoning trace while retaining its token count in usage metadata.

Development

uv sync --all-groups
uv run ruff check .
uv run ruff format --check .
uv run pytest

Tests use a mocked OpenRouter transport and a real MCP stdio handshake; they do not spend API credits. Retry tests cover network failures, rate limits, every 5xx status, malformed or empty successful responses, and OpenRouter's occasional transient invalid model ID response for an otherwise live model slug. OpenRouter can report provider failures inside HTTP 200 responses; those are classified by their embedded status so billing, authentication, guardrail, and token-cap errors are not retried. Explicit transient provider responses and connection-establishment failures use the full retry budget. Ambiguous failures that may already have incurred usage are retried at most once, and read timeouts are not retried. Backoff is bounded; Retry-After supports both delta-seconds and HTTP dates up to five minutes, while longer cooldowns fail without retrying early. Connection and pool waits are capped at 30 seconds and request writes at 60 seconds, independently of the longer response-read timeout needed for high-effort K3 completions. The server's total call deadline is deliberately 10 minutes shorter than the sample MCP client timeout, leaving time to return a structured error instead of being cancelled by the client.

GitHub Actions runs linting, formatting, tests, the stdio protocol handshake, and package builds on Python 3.11, 3.12, and 3.13. Workflow actions are pinned to immutable commit SHAs.

Security and cost

• Tool calls send the supplied code and project context to OpenRouter and its selected provider.

• The server is read-only but calls a metered external API (openWorldHint=true).

• The 256,000-token completion setting is a ceiling, not a reservation, but long reasoning calls can still consume substantial metered output tokens.

• Inputs and outputs are bounded by environment-configurable limits.

• Errors never include request headers or the API key.

• Model output is advisory. Verify findings and plans before applying changes.