subgraph-explorer

What this skill does

# Subgraph Explorer

## Overview

This skill enables exploration and querying of blockchain subgraphs through a private MCP server. It provides tools for managing the Docker-based server, exploring GraphQL schemas, executing queries against configured subgraphs, and exporting discovered queries for project integration.

## Quick Start

### Starting the MCP Server

Before using subgraph exploration features, ensure the MCP server is running:

```bash
bash scripts/start_mcp_server.sh
```

This starts the Docker container with:
- **SSE endpoint**: `http://localhost:8000` (for MCP communication)
- **Metrics endpoint**: `http://localhost:9091/metrics` (for monitoring)

Check server status:
```bash
bash scripts/check_mcp_status.sh
```

Stop the server:
```bash
bash scripts/stop_mcp_server.sh
```

**Note**: The scripts default to `~/Workspace/subgraph-mcp` as the project path. Set `SUBGRAPH_MCP_PATH` environment variable to override.

### MCP Server Connection

The MCP server runs in SSE mode and exposes the following tools via HTTP:

**Registry-based tools:**
- `list_subgraphs` - List all configured subgraphs
- `search_subgraphs_by_keyword` - Search subgraphs by keyword
- `get_schema_by_id` - Get GraphQL schema for a configured subgraph
- `execute_query_by_id` - Execute query against a configured subgraph
- `get_query_examples_by_id` - Get query examples for a subgraph
- `get_subgraph_guidance_by_id` - Get subgraph-specific guidance

**Ad-hoc tools:**
- `get_schema_by_url` - Get schema from any GraphQL endpoint (no registry needed)
- `execute_query_by_url` - Execute query against any GraphQL endpoint (no registry needed)

To interact with the MCP server, use the WebFetch tool to make HTTP requests to the SSE endpoint at `http://localhost:8000`.

## Core Workflows

### 1. Exploring Configured Subgraphs

When exploring subgraphs in the registry (`subgraphs.json`):

**Step 1: List or Search**
- Use `list_subgraphs` to see all available subgraphs
- Use `search_subgraphs_by_keyword` to find specific subgraphs by name/description

**Step 2: Understand the Schema**
- Use `get_schema_by_id` to retrieve the GraphQL schema
- Examine entity types, fields, and relationships
- Check `get_query_examples_by_id` for pre-built query templates
- Review `get_subgraph_guidance_by_id` for subgraph-specific tips

**Step 3: Execute Queries**
- Use `execute_query_by_id` to run GraphQL queries
- Start with simple queries and iterate
- Apply pagination for large result sets
- Reference `references/graphql_patterns.md` for common patterns

**Step 4: Export Useful Queries**
- Use `scripts/export_query.py` to save queries for project use
- Choose format: JavaScript, Python, GraphQL, or JSON

### 2. Ad-hoc Subgraph Exploration

For exploring subgraphs not in the registry:

**Direct URL Access:**
- Use `get_schema_by_url` with the GraphQL endpoint URL
- Optionally provide `auth_header` if authentication is required
- Use `execute_query_by_url` to run queries directly

Example workflow:
1. Get schema: `get_schema_by_url(url="https://example.com/graphql")`
2. Analyze available entities and fields
3. Build query based on schema
4. Execute: `execute_query_by_url(url="https://example.com/graphql", query="...", variables={...})`

### 3. Query Development Process

**Iterative Query Building:**

1. **Start Simple**: Query a single entity to understand data structure
   ```graphql
   query SimpleQuery {
     entity(id: "0x123") {
       id
       name
     }
   }
   ```

2. **Add Fields**: Gradually add more fields as needed
   ```graphql
   query ExpandedQuery {
     entity(id: "0x123") {
       id
       name
       timestamp
       relatedData {
         field1
         field2
       }
     }
   }
   ```

3. **Apply Filters**: Use `where` clauses for specific criteria
   ```graphql
   query FilteredQuery($minValue: String!) {
     entities(where: { value_gte: $minValue }, first: 100) {
       id
       value
       timestamp
     }
   }
   ```

4. **Optimize**: Use aggregated fields instead of large arrays
   - Prefer: `contract.holders` (pre-calculated count)
   - Avoid: Counting all `tokens` manually

**Reference**: See `references/graphql_patterns.md` for comprehensive query patterns including pagination, filtering, aggregation, and performance optimization.

## Exporting Queries

Use the export utility to save discovered queries for project integration:

### JavaScript/TypeScript Export
```bash
python3 scripts/export_query.py queries/getLatestSwaps.js --format js --name GetLatestSwaps --description "Fetch latest DEX swaps"
```

Then paste your GraphQL query when prompted.

Output:
```javascript
/**
 * Fetch latest DEX swaps
 */
export const GetLatestSwaps = `
  query GetLatestSwaps($first: Int!) {
    swaps(first: $first, orderBy: timestamp, orderDirection: desc) {
      id
      timestamp
      amountUSD
      pair {
        token0 { symbol }
        token1 { symbol }
      }
    }
  }
`;
```

### Python Export
```bash
python3 scripts/export_query.py queries/get_latest_swaps.py --format py --name get_latest_swaps
```

### GraphQL File Export
```bash
python3 scripts/export_query.py queries/latest_swaps.graphql --format graphql
```

### JSON Export (with metadata)
```bash
python3 scripts/export_query.py queries/latest_swaps.json --format json --name GetLatestSwaps --variables '{"first": 100}'
```

## Understanding Subgraph Data

### Common Entity Types

**DEX/Trading Subgraphs:**
- `Swap` - Individual trade transactions
- `Pair` - Trading pairs with liquidity and volume
- `Token` - Token information and metadata
- `DayData` / `PairDayData` - Aggregated daily metrics

**NFT Subgraphs:**
- `ERC721Transfer` / `ERC1155Transfer` - NFT transfer events
- `Account` - User accounts with balances
- `ERC721Token` / `ERC1155Token` - Individual NFT tokens
- `ERC721Contract` - NFT collection contracts

**Common Patterns:**
- Most entities have `id`, `timestamp`, and `blockNumber` fields
- Use relationship fields (e.g., `pair { token0 { symbol } }`) to navigate connections
- Aggregated fields (e.g., `totalSupply`, `holders`) provide pre-calculated stats

### Data Considerations

**Time-based Data:**
- Daily aggregates typically reset at midnight UTC
- For "today's" data, consider querying both current day and previous day
- Calculate partial day metrics: `(yesterday_value * hours_passed / 24) + today_value`

**Pagination:**
- Maximum `first` parameter is typically 1000, recommended 100
- Use `skip` for offset-based pagination
- Use cursor-based pagination (`id_gt`) for large datasets

**Performance:**
- Avoid deep nesting of relationships
- Use aggregated fields when available
- Apply specific filters to reduce result sets
- Request only needed fields, not entire objects

## Troubleshooting

### MCP Server Issues

**Container won't start:**
- Check if ports 8000 or 9091 are already in use
- Verify `subgraphs.json` exists in the subgraph-mcp directory
- View logs: `docker logs subgraph-mcp-server`

**Server not responding:**
- Run: `bash scripts/check_mcp_status.sh`
- Verify Docker is running
- Check firewall settings for localhost access

**Configuration errors:**
- Verify `SUBGRAPH_MCP_PATH` points to correct directory
- Ensure `subgraphs.json` is valid JSON
- Check subgraph URLs are accessible

### Query Issues

**Schema introspection fails:**
- Verify the subgraph endpoint is accessible
- Check authentication headers if required
- Ensure the endpoint is a valid GraphQL API

**Query timeout:**
- Simplify the query (reduce nesting, fewer fields)
- Add more specific filters
- Reduce `first` parameter value
- Use pagination for large datasets

**Type errors:**
- Check field types in schema (String vs Int vs BigInt)
- Ensure variable types match schema requirements
- Use quotes for String types in variables: `{"id": "0x123"}`

**Empty results:**
- Verify entity IDs are correct (case-sensitive)
- Check filter conditions aren't too restrictive
- For time-based queries, verify timestamp format (usually Uni