> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pylar.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Editing MCP Tools

> Learn how to view, edit, and refine MCP tools in Pylar

## Overview

After creating an MCP tool (either with AI or manually), you'll often want to refine it. This guide shows you how to access, view, and edit your tools.

## Accessing Your Tools

To view and edit a tool:

1. Navigate to your project in Pylar
2. Look for the **"MCP Tools"** section in the right sidebar
3. **Click on the tool** you want to view or edit

The tool details will open, showing all its components.

## Tool Components Overview

When you open a tool, you'll see:

### Function Name

* Located at the top
* Can be renamed by clicking and editing
* Should be descriptive and unique

### Description

* Summary of what the tool does
* Visible to AI agents
* Can be edited inline

### SQL Query

* The query that retrieves data
* Can be edited in the query editor
* Supports parameter placeholders

### Parameters

* Input definitions for the tool
* Can add, remove, or modify parameters
* Define types and requirements

### Tool Call Arguments

* Test values (right side)
* Used for testing
* Can be modified

## Editing Function Name

To rename a tool:

1. Click on the function name at the top
2. Edit the name directly
3. Save your changes

<Tip>
  Choose names that clearly indicate the tool's purpose. Agents use function names to identify and call tools.
</Tip>

## Editing Description

The description helps agents understand when to use your tool.

**To edit**:

1. Click on the description field
2. Modify the text
3. Save changes

**Best Practices**:

* Be specific about what data is returned
* Mention any important behaviors (e.g., "supports partial matching")
* Keep it concise but informative

**Example**:

* ❌ "Gets data" (too vague)
* ✅ "Fetches engagement scores and related data filtered by event type" (clear and specific)

## Editing SQL Query

The SQL query is where you define how the tool retrieves data.

### Accessing the Query Editor

1. Find the **SQL Query** section in the tool view
2. Click to edit (or use the edit button)
3. Modify the query as needed
4. Save your changes

### Common Edits

**Adding Filters**:

```sql theme={null}
-- Original
SELECT engagement_score FROM table0 WHERE event_type = '{event_type}'

-- Enhanced
SELECT engagement_score FROM table0 
WHERE event_type = '{event_type}' 
  AND date >= '{start_date}'
  AND engagement_score > {min_score}
```

**Changing Sort Order**:

```sql theme={null}
-- Ascending instead of descending
ORDER BY engagement_score ASC
```

**Adding Columns**:

```sql theme={null}
-- Original
SELECT engagement_score FROM table0

-- Enhanced
SELECT engagement_score, event_id, timestamp, user_id 
FROM table0
```

**Improving Performance**:

```sql theme={null}
-- Add LIMIT for large result sets
SELECT engagement_score FROM table0 
WHERE event_type = '{event_type}'
ORDER BY engagement_score DESC
LIMIT 100
```

<Warning>
  When editing queries, ensure parameter placeholders match parameter names exactly. Mismatched names will cause errors.
</Warning>

## Modifying Parameters

Parameters define what inputs your tool accepts.

### Adding Parameters

1. Go to the **Parameters** section

2. Click **Add Parameter** or similar

3. Define:
   * Parameter name
   * Type (string, number, etc.)
   * Whether it's required
   * Description

4. Update your SQL query to use the new parameter

**Example**: Adding a date range parameter

```sql theme={null}
-- Query with new parameter
SELECT * FROM table0 
WHERE event_type = '{event_type}'
  AND date >= '{start_date}'
  AND date <= '{end_date}'
```

### Removing Parameters

1. Find the parameter in the Parameters section
2. Remove it
3. Update your SQL query to remove the placeholder

<Info>
  If you remove a parameter, make sure to also remove its placeholder from the SQL query. Otherwise, the query will fail when executed.
</Info>

### Modifying Parameter Properties

You can change:

* **Type**: string → number, etc.
* **Required/Optional**: Make parameters required or optional
* **Description**: Update what the parameter represents

## Updating Tool Call Arguments

Tool call arguments are test values used for verification.

**To update**:

1. Find the **Tool Call Arguments** section (right side)
2. Modify the test values
3. Use these values when testing the tool

**Example**:

```json theme={null}
{
  "event_type": "login"  // Change to "purchase" to test different scenarios
}
```

<Tip>
  Use varied test arguments to verify your tool handles different inputs correctly. Test edge cases like empty strings, special characters, etc.
</Tip>

## Common Editing Scenarios

### Scenario 1: Refining an AI-Generated Tool

After AI creates a tool, you might want to:

1. **Add more filters**: Include additional WHERE conditions
2. **Change sort order**: Modify ORDER BY clause
3. **Add columns**: Return more data
4. **Improve description**: Make it clearer for agents

### Scenario 2: Fixing a Tool That's Not Working

If a tool fails testing:

1. **Check parameter names**: Ensure placeholders match parameter names
2. **Verify SQL syntax**: Test query manually in SQL IDE
3. **Check data types**: Ensure parameter types match query expectations
4. **Review filters**: Make sure WHERE conditions are valid

### Scenario 3: Optimizing Performance

For slow queries:

1. **Add LIMIT**: Restrict result set size
2. **Refine filters**: Make WHERE conditions more selective
3. **Remove unnecessary columns**: Select only needed data
4. **Add indexes**: Ensure source views have proper indexes

## Viewing JSON Structure

To see the complete tool configuration:

1. Click **"JSON view"** button
2. View the structured JSON representation
3. Useful for:
   * Understanding complete configuration
   * Debugging issues
   * Copying tool structure

**Example JSON View**:

```json theme={null}
{
  "function_name": "fetch_engagement_scores_by_event_type",
  "description": "Fetches engagement scores and related data filtered by event type",
  "query": "SELECT engagement_score FROM table0 WHERE event_type LIKE '%{event_type}%' \nORDER BY engagement_score DESC",
  "parameters": {
    "type": "object",
    "required": ["event_type"],
    "properties": {
      "event_type": {
        "type": "string",
        "description": "Event type to filter by (supports partial matching)"
      }
    }
  },
  "tool_call": {
    "name": "fetch_engagement_scores_by_event_type",
    "args": {
      "event_type": "login"
    }
  }
}
```

## Best Practices

### Before Editing

* ✅ Test the current tool to understand its behavior
* ✅ Review the SQL query in the SQL IDE
* ✅ Check parameter usage in the query

### While Editing

* ✅ Make incremental changes
* ✅ Test after each significant change
* ✅ Keep parameter names consistent
* ✅ Update descriptions when behavior changes

### After Editing

* ✅ Always test the tool after changes
* ✅ Verify parameters work correctly
* ✅ Check that results are as expected
* ✅ Update descriptions if needed

## Next Steps

After editing your tool:

* [Testing Your Tools](/learn/building-mcp-tools/testing-your-tools) - Verify your changes work correctly
* [Publishing Tools](/learn/publishing-tools/overview) - Make your tools available to agents

<Card title="Test Your Changes" icon="flask" href="/learn/building-mcp-tools/testing-your-tools">
  Verify your edited tool works correctly
</Card>