Limelight

Give your AI coding assistant runtime context from your running app

The Limelight MCP Server connects your running React or React Native app to AI coding assistants likeCursor,Claude Code, and any MCP-compatible editor.

Instead of copying logs into ChatGPT or hoping your AI can guess what's wrong from source code alone, Limelight streams live runtime data — renders, state changes, network requests, and console logs — directly into your editor's AI.

Ask your AI"why is my app slow?"and it answers with real data, not guesses.

The MCP server runs locally on your machine. No data leaves your system.

<Tab title="Cursor"> Add to your MCP settings: 

Add Zustand or Redux stores to capture state changes:

Limelight.connect({ target: "mcp", stores: { authStore: useAuthStore, cartStore: useCartStore }, });

 "My app feels slow. Do you see any issues?"  "Why is my search showing wrong results?"  "Which components are re-rendering the most?"* Your AI will call Limelight's tools automatically to inspect your app's runtime state and give you answers backed by real data.

Once connected, your AI assistant has access to everything happening in your running app:

The MCP server exposes 11 tools that your AI calls automatically based on your questions. For detailed usage workflows and examples, see theTools & Workflowsguide.

Returns: total events by type, error/warning counts, top rendered components, suspicious items, and session metadata.

Detects: unnecessary re-renders, unstable props, render cascades, race conditions, N+1 queries, state thrashing, and more. | Parameter | Type | Default | Description | | ------------- | ------- | ------- | -------------------------------------- | | verbose | boolean | false | Include causal summaries and event IDs | | limit | number | 5 | Max issues to return | | deduplicate | boolean | true | Group similar issues together |

| Parameter | Type | Default | Description | | --------------- | ------------------------ | --------------- | ---------------------------- | | error_id | string | — | Specific event ID | | error_pattern | string | — | Match against error messages | | scope | "most_recent" \| "all" | "most_recent" | Which errors to analyze |

| Parameter | Type | Default | Description | | ----------------- | -------------- | ------- | ------------------------------- | | url_pattern | string | — | URL substring or pattern | | method | string | — | HTTP method filter | | status_range | { min, max } | — | Status code range | | min_duration_ms | number | — | Slow request threshold | | include_bodies | boolean | false | Include request/response bodies | | limit | number | 10 | Max results |

| Parameter | Type | Default | Description | | ---------------------- | ------------------------------------------------- | ------- | ---------------------- | | level | "error" \| "warn" \| "log" \| "info" \| "debug" | — | Log level filter | | message_pattern | string | — | Search within messages | | include_stack_traces | boolean | auto | Include stack traces | | limit | number | 10 | Max results |

| Parameter | Type | Default | Description | | ---------------- | -------------------------------- | ------- | ------------------ | | last_n_seconds | number | 10 | Time window | | event_types | array | all | Filter by type | | min_severity | "info" \| "warning" \| "error" | — | Minimum importance |

| Parameter | Type | Description | | ---------------- | ------ | ------------------------ | | component_name | string | Component to investigate |

| Parameter | Type | Default | Description | | ----------------- | ----------------------------------------------- | --------------- | ----------------------- | | component_name | string | — | Filter to one component | | suspicious_only | boolean | false | Only flagged components | | sort_by | "render_count" \| "render_cost" \| "velocity" | "render_cost" | Sort order | | limit | number | 10 | Max results |

| Parameter | Type | Default | Description | | ----------------- | ------- | ------- | ---------------------------- | | store_id | string | — | Specific store | | path | string | — | Dot-notation path into state | | include_history | boolean | false | Include recent changes | | history_limit | number | 10 | Number of recent changes |

| Parameter | Type | Default | Description | | ---------- | ------- | ------- | ----------------------------------- | | event_id | string | — | Event to correlate (required) | | verbose | boolean | false | Full graph with all nodes and edges |

| Parameter | Type | Description | | ---------- | ------ | ------------------------------------------ | | event_id | string | The ID of the event to retrieve (required) |

The MCP server accepts CLI arguments for customization:

npx limelight-mcp --port 9229 --max-events 10000 --verbose

Your App (with SDK) → WebSocket → Limelight MCP Server → stdio → AI Editor

- The Limelight SDK captures runtime events in your app
- Events stream to the MCP server over a local WebSocket connection
- The MCP server runs correlation and analysis on the events
- Your AI assistant calls Limelight's tools via the MCP protocol
- Responses include structured, pre-analyzed debugging context — not raw logs

All data stays on your machine. The MCP server runs locally and communicates with your editor over stdio.

The MCP server stores events in memory. Data resets when the server restarts. Maximum capacity is configurable with--max-events.

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