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oni-chan69

kimi-code-mcp

by oni-chan69

kimi_analyze

Understand large codebases by sending a prompt; receive a structured analysis report with key findings and code insights.

Instructions

Send a prompt to Kimi Code for codebase analysis. Kimi reads the codebase (256K context) and returns a compressed, structured report.

CACHE BEHAVIOR: If session_id is not provided, the MCP server will automatically use cached sessions when available.

  • First call: Creates cache (may take 60-120s for large codebases)

  • Subsequent calls: Reuses cached session (faster, ~10s)

  • Cache auto-expires after 30 minutes or when files change

  • Use kimi_cache_status to view cache statistics

Output is budget-controlled: Kimi reads 200K+ tokens of source but returns a 5-15K token analysis (configurable via detail_level). Use kimi_resume to drill deeper into specific areas. Takes 1-5 minutes for large codebases.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe analysis prompt for Kimi (be specific about what to analyze)
work_dirYesAbsolute path to the codebase root directory
session_idNoResume a specific Kimi session by ID (from kimi_list_sessions). If not provided, cached session will be used when available.
thinkingNoEnable thinking mode for deeper analysis (default: true)
detail_levelNoOutput verbosity. summary: ~2-5K tokens (file index + key findings). normal (default): ~5-15K tokens (structured analysis). detailed: ~15-40K tokens (with code snippets).
max_output_tokensNoMax tokens in response (~4 chars/token). Default: 15000. Use 3000-5000 for quick scans, 30000+ for detailed analysis.
include_thinkingNoInclude Kimi internal reasoning in output. Default: false (saves 10-30K tokens). Enable only for debugging.
use_cacheNoEnable automatic session caching (default: true). Set to false to bypass cache and create fresh session.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description fully covers behavioral traits: cache behavior (auto-cache, expiration, performance), output budget control (detail_level, max_output_tokens), and estimated execution times (60-120s for first call, ~10s subsequent, 1-5 minutes for large codebases).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose statement followed by structured items on cache behavior and output details. It is relatively long but front-loaded with the main purpose, earning a 4 for good organization.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 8 parameters, no output schema, and no annotations, the description is highly complete. It covers tool purpose, caching mechanics, output configuration, performance expectations, and related tools, ensuring the agent can use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage with descriptions for all 8 parameters. The description adds context about caching and output budget but does not significantly enhance parameter meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool sends a prompt to Kimi Code for codebase analysis and returns a compressed, structured report. It distinguishes itself from siblings like kimi_resume and kimi_query by mentioning their use cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides guidance on when to use this tool (first vs. subsequent calls) and suggests using kimi_resume for deeper analysis. However, it does not explicitly state when not to use this tool or provide comprehensive alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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