Graph explore

Graph Explore

Reference for code-review-graph MCP tool operations. Other skills call graph MCP tools directly in the main conversation — no dedicated agents.

How Other Skills Use This

Calling skills load graph tools via ToolSearch (search for "code-review-graph") and call them directly. The typical pattern:

1. build_or_update_graph_tool — ensure graph is fresh
2. semantic_search_nodes_tool — search with feature keywords (2-3 variations)
3. query_graph_tool with file_summary — for relevant domain directories
4. query_graph_tool with imports_of / importers_of — for key files

Each calling skill has the tool sequences inline. This skill serves as the detailed reference for available modes and safety rules.

Task types:

• explore "<feature keywords>" <repo-root> — Full feature discovery: semantic search, file summaries, dependency mapping
• impact "<file1> <file2> ..." — Blast radius analysis for changed files
• review — Post-develop review context (auto-detects changed files from git)

Input

The user input is: $ARGUMENTS

• Task type + arguments: As described above
• Empty: Ask the caller what to explore

Process

Step 1: Load Graph Tools

Load these tools via ToolSearch at the start:

• mcp__plugin_code-review-graph_code-review-graph__build_or_update_graph_tool
• mcp__plugin_code-review-graph_code-review-graph__semantic_search_nodes_tool
• mcp__plugin_code-review-graph_code-review-graph__query_graph_tool
• mcp__plugin_code-review-graph_code-review-graph__embed_graph_tool
• mcp__plugin_code-review-graph_code-review-graph__get_impact_radius_tool
• mcp__plugin_code-review-graph_code-review-graph__get_review_context_tool

If tools are not available (MCP server not connected), return: "GRAPH_UNAVAILABLE: code-review-graph MCP server not connected."

Step 2: Ensure Graph is Fresh

Run build_or_update_graph_tool (incremental — fast if already up-to-date).

Step 3: Ensure Embeddings Exist

Run a test search with semantic_search_nodes_tool using a common keyword (e.g., "service").

If search_mode in the response is "keyword" (not "semantic"), embeddings are missing. Run embed_graph_tool to compute them. This is a one-time operation (~30s for ~1000 nodes).

If search_mode is "semantic", embeddings are ready.

Step 4: Execute Task

Mode: explore

Goal: Map existing code related to a feature, so the caller can design without reading files.

1. Semantic search — Run semantic_search_nodes_tool with the feature keywords. Try 2-3 keyword variations if first returns few results (e.g., "referral invite" then "email send notification" then "discount credit").
2. Map structure — For each relevant domain directory found, run query_graph_tool with file_summary to list all files and their contents.
3. Trace dependencies — For the most relevant files, run query_graph_tool with imports_of and importers_of to understand how they connect.
4. Consolidate — Return a structured summary organized by domain.

Output format:

## Graph Exploration: [keywords]

### Relevant Code Found
- [file path] — [what it contains, key exports]
- ...

### Domain Structure
- domain/X/ — [summary of what's in this domain]
- ...

### Key Dependencies
- [file A] imports [file B] (for [purpose])
- ...

### Suggested Starting Points
- [file] — [why it's relevant to the feature]

Safety rules:

• Do NOT use get_architecture_overview_tool, list_communities_tool, or detect_changes_tool — they can return 90-300K+ chars and overflow context.
• Limit file_summary queries to specific domain directories, not the entire src/.
• Cap at 5-6 query_graph_tool calls to avoid excessive output.

Mode: impact

Goal: Analyze blast radius of changed files for plan review or risk assessment.

1. Run get_impact_radius_tool with the provided file list.
2. Identify high-risk files (many dependents, critical paths).
3. Return structured summary of what's affected.

Output format:

## Impact Analysis

### Changed Files
- [file] — [direct impact count] dependents

### High-Risk Areas
- [file] is imported by [N] files — changes here affect [list]

### Recommended Review Focus
- [file] — [reason it's risky]

Mode: review

Goal: Generate token-efficient review context for post-develop review.

1. Run build_or_update_graph_tool to capture new files.
2. Run get_review_context_tool (auto-detects changed files from git).
3. Extract the high-risk files and review guidance.
4. Return the review context summary (not full source — the LLM agent reads files).

Output format:

## Review Context

### Changed Files
- [file] — [change summary]

### High-Risk Files (review these first)
- [file] — [reason: N importers, critical path, etc.]

### Review Guidance
- [specific things to check based on graph analysis]

Step 5: Return

Return the structured summary to the caller. Be concise — the caller will use this to inform design decisions, not as exhaustive documentation.

Prerequisites

Embeddings require sentence-transformers in the MCP server environment. The code-review-graph plugin's .mcp.json must include it:

{
  "mcpServers": {
    "code-review-graph": {
      "command": "uvx",
      "args": ["--with", "sentence-transformers", "code-review-graph", "serve"]
    }
  }
}

Without this, embed_graph_tool will fail and semantic search falls back to keyword matching. The skill still works — it uses file_summary and query_graph_tool as alternatives — but results are less accurate.

This is a one-time global setup. After adding --with sentence-transformers, restart Claude Code for the MCP server to pick it up.

Notes

• Graph queries are fast (~10-50ms each) — the main cost is token consumption, not time
• Embeddings use all-MiniLM-L6-v2 by default (~80MB model, downloaded once per project)
• After embedding, semantic search finds conceptually related code, not just keyword matches
• The agent typically makes 8-12 tool calls and returns a ~500-word summary
• If embed_graph_tool fails (missing dependency), the agent should log a warning and continue with keyword search — do not block the pipeline