MCP Tools Overview

dev-agent provides nine tools through the Model Context Protocol (MCP). These tools give AI assistants deep understanding of your codebase.

Available Tools

Tool	Purpose
`dev_search`	Semantic code search with snippets
`dev_refs`	Query code relationships (callers/callees)
`dev_map`	Codebase structure overview with change frequency
`dev_history`	Semantic search over git commits ✨ v0.4
`dev_plan`	Assemble context for GitHub issues
`dev_explore`	Explore code patterns and relationships
`dev_gh`	Search GitHub issues and PRs
`dev_status`	Check repository indexing status
`dev_health`	Monitor MCP server health

New in v0.5.0

Enhanced indexing — Arrow functions, React hooks, and exported constants now extracted
dev_search — Better coverage of modern JavaScript patterns (hooks, utilities, configs)

v0.4.0

dev_history — Semantic search over git commit history
dev_map — Change frequency indicators (🔥 hot, ✏️ active, 📝 recent)
dev_plan — Related commits included in context assembly

v0.3.0

dev_refs — Query what calls what and what is called by what
dev_map — Get codebase overview with hot paths and smart depth
dev_plan — Refactored to assemble context (no more heuristic task breakdown)

How Tools Are Exposed

Tools are exposed via the Model Context Protocol (MCP), a standard for AI tool integration:


AI Assistant (Cursor/Claude)
        │
        │ MCP Protocol (stdio)
        ▼
   dev-agent MCP Server
        │
        ├── SearchAdapter      → dev_search
        ├── RefsAdapter        → dev_refs
        ├── MapAdapter         → dev_map
        ├── HistoryAdapter     → dev_history  ✨ v0.4
        ├── PlanAdapter        → dev_plan
        ├── ExploreAdapter     → dev_explore
        ├── GitHubAdapter      → dev_gh
        ├── StatusAdapter      → dev_status
        └── HealthAdapter      → dev_health

Tool Response Format

All tools return structured responses with metadata:


{
  "success": true,
  "data": {
    "query": "authentication",
    "content": "1. [89%] class: AuthMiddleware..."
  },
  "metadata": {
    "tokens": 245,
    "duration_ms": 123,
    "results_total": 15,
    "results_returned": 10
  }
}

The metadata field helps AI assistants understand:

Token cost of the response
Performance metrics
Result completeness (truncated or full)

Output Formats

Most tools support two output formats:

compact (default) — Token-efficient, markdown output
verbose — Full JSON with all details

Next Steps

dev_search → — Semantic code search
dev_history → — Git commit search
dev_refs → — Relationship queries
dev_map → — Codebase overview