cursor-explorer-mcp
This server performs deep codebase analysis and cross-file implementation tracing for complex query requirements.
Install
mkdir -p .claude/skills/cursor-explorer-mcp && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/307" && unzip -o skill.zip -d .claude/skills/cursor-explorer-mcp && rm skill.zipInstalls to .claude/skills/cursor-explorer-mcp
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use for token-expensive operations requiring multi-file analysis - codebase exploration, broad searches, architecture understanding, tracing flows, finding implementations across files. Uses MCP cursor-agent server (company pays) with clean async interface. Do NOT use for single-file analysis, explaining code already in immediate context, or pure reasoning tasks.Key capabilities
- →Multi-file codebase exploration
- →Architecture flow tracing
- →Cross-file implementation search
- →Dependency analysis
How it works
The skill utilizes a dedicated MCP server to perform asynchronous, batch-processed queries across multiple files to map dependencies and implementation details.
Inputs & outputs
When to use cursor-explorer-mcp
- →Finding implementation details across multiple files
- →Tracing code flow throughout the architecture
- →Performing complex code searches
- →Understanding inter-module dependencies
About this skill
Cursor Explorer (MCP)
Trigger immediately when you see:
- "Find where X is..." → cursor-agent
- "How does X work?" (multi-file) → cursor-agent
- "Trace the flow of..." → cursor-agent
- Manual approach needs 3+ file reads → cursor-agent
Skip for: single file, pure reasoning, code in context, 1-2 line answers
Workflow
# 1. Start query (batch multiple questions)
start = mcp__cursor_agent__cursor_agent_start({
"query": "Find where X is. Give file:line, code snippets, purpose."
})
query_id = json.loads(start)["query_id"]
# 2. Get result (blocks until done)
result = mcp__cursor_agent__cursor_agent_result({
"query_id": query_id,
"wait": True # Blocks automatically, no manual monitoring needed
})
output = json.loads(result)
# 3. If completed, present findings. If failed, fall back to Read/Grep.
Never retry on failure - just fall back to manual tools.
Query Tips
- Request file:line refs
- Ask for code snippets
- Batch related questions
- Be specific about format needed
When not to use it
- →For single-file analysis
- →For explaining code already present in the immediate context
- →For pure reasoning tasks
Prerequisites
Limitations
- →Requires fallback to manual tools if the agent query fails
- →Not suitable for simple, localized code questions
How it compares
It offloads token-intensive, multi-file analysis to a specialized server rather than relying on the agent's immediate context window.
Compared to similar skills
cursor-explorer-mcp side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| cursor-explorer-mcp (this skill) | 6 | 8mo | No flags | Intermediate |
| analyzing-projects | 3 | 6mo | Review | Beginner |
| octocode-research | 0 | 2mo | Review | Advanced |
| research-engineer | 2 | 6mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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