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UrbanDiver

Local DeepWiki MCP Server

by UrbanDiver

analyze_diff

Read-onlyIdempotent

Analyze git diff between two refs to map changed files to wiki pages and code entities, or answer questions about the diff using RAG.

Instructions

Analyze git diff between two refs. Supports two modes:

  • mode='structured' (default): Map changed files to affected wiki pages and code entities. Returns structured analysis.

  • mode='question': Ask questions about the diff using RAG. Combines git diff with vector search context and LLM synthesis. Requires 'question' parameter.

No prior indexing required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_pathYesPath to the repository (must be a git repo)
modeNoAnalysis mode: 'structured' for file/entity mapping, 'question' for natural-language Q&A (default: structured)
questionNoQuestion about the code changes (required when mode='question')
base_refNoGit ref to diff from (default: HEAD~1)
head_refNoGit ref to diff to (default: HEAD)
include_contentNoInclude diff content for each file (default: false, only for mode='structured')
max_contextNoMaximum code chunks for context (default: 10, max: 30, only for mode='question')
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description complements these by clarifying mode-specific behavior (e.g., include_content only for structured, max_context only for question) and that no prior indexing is required. No contradictions.

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

Conciseness5/5

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

The description is concise with two short paragraphs. The first sentence captures the core purpose. Modes are listed with bullet-like clarity. Every sentence adds information, with no waste.

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

Completeness4/5

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

Given the tool's complexity and 7 parameters, the description covers modes, defaults, mode-specific parameter constraints, and the no-indexing requirement. It lacks an explicit description of the output format, but the modes imply structured or LLM-generated text. Without an output schema, the description is nearly complete.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for all 7 parameters. The description adds value by explaining mode-specific conditional requirements (e.g., question required when mode='question') and defaults (base_ref defaults to HEAD~1). This goes 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 clearly states the tool analyzes git diffs between two refs and details two distinct modes: structured for file/entity mapping, and question for natural-language Q&A. This distinguishes it from sibling tools like ask_about_diff.

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 explains when to use each mode (structured for mapping, question for Q&A) and notes that question mode requires the 'question' parameter. It also states 'No prior indexing required.' It lacks explicit when-not-to-use but provides clear context for decision-making.

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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