@aegis-ai/mcp-toolselect

An MCP server that recommends which tools to use for a given task. It learns from usage patterns and adapts recommendations over time based on success rates.

Install

bun install @aegis-ai/mcp-toolselect

Or clone and run directly:

git clone https://github.com/aegis-ai/mcp-toolselect.git
cd mcp-toolselect
bun install
bun src/index.ts

Configuration

Add to your MCP client config (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "toolselect": {
      "command": "bunx",
      "args": ["@aegis-ai/mcp-toolselect"]
    }
  }
}

Or if running from source:

{
  "mcpServers": {
    "toolselect": {
      "command": "bun",
      "args": ["/path/to/mcp-toolselect/src/index.ts"]
    }
  }
}

Tools

recommend_tools

Get ranked tool recommendations for a task description. Returns confidence scores, priority levels, and historical success rates.

Parameters:

task (string, required) - Description of the task
max_results (number, optional) - Max recommendations to return (default: 5)

Example:

{
  "task": "Write integration tests for the payment API",
  "max_results": 3
}

Response:

{
  "task": "Write integration tests for the payment API",
  "analysis": {
    "keywords": ["testing", "coding", "api"],
    "complexity": "medium",
    "estimatedDuration": "medium"
  },
  "recommendations": [
    {
      "tool": "jest",
      "confidence": 0.85,
      "reason": "matches keyword \"testing\"; strength \"integration tests\" found in task",
      "priority": "required",
      "successRate": 0.92,
      "timesUsed": 47
    }
  ]
}

register_tool

Parameters:

name (string, required) - Unique tool name
description (string, required) - What the tool does
category (string, required) - Category (e.g. coding, testing, deployment, research, analysis)
strengths (string[], required) - What the tool is good at
use_cases (string[], required) - Typical scenarios where the tool shines

Example:

{
  "name": "playwright",
  "description": "Browser automation and end-to-end testing framework",
  "category": "testing",
  "strengths": ["browser automation", "e2e testing", "cross-browser", "screenshot comparison"],
  "use_cases": ["end-to-end tests", "visual regression testing", "web scraping", "form automation"]
}

record_usage

Record that a tool was used for a task and whether it succeeded. This feedback drives future recommendation quality.

Parameters:

tool (string, required) - Tool name
task (string, required) - Task description
success (boolean, required) - Whether the tool completed the task successfully
duration_ms (number, optional) - Execution time in milliseconds
notes (string, optional) - Additional context

Example:

{
  "tool": "playwright",
  "task": "Run e2e tests for checkout flow",
  "success": true,
  "duration_ms": 12500,
  "notes": "All 15 tests passed"
}

get_tool_stats

Get usage statistics and success rates for registered tools.

Parameters:

tool (string, optional) - Specific tool name. Omit to get all stats.

Example response:

{
  "totalTools": 8,
  "totalUsages": 142,
  "tools": [
    {
      "name": "playwright",
      "timesUsed": 47,
      "successCount": 43,
      "failCount": 4,
      "avgDurationMs": 11200,
      "overallSuccessRate": 0.91,
      "contextSuccessRates": {
        "testing": 0.94,
        "debugging": 0.78
      }
    }
  ]
}

list_tools

List all registered tools grouped by category.

Parameters:

category (string, optional) - Filter by category

How It Works

Register tools with their capabilities and use cases
Ask for recommendations by describing your task
Record outcomes after using a tool (success/failure)
The system learns which tools work best for which types of tasks and adjusts future confidence scores accordingly

The recommendation engine:

Analyzes task descriptions to extract keywords and estimate complexity
Matches keywords against registered tool strengths and use cases
Adjusts confidence using historical success rates (exponential moving average)
Returns prioritized recommendations sorted by relevance

Data Storage

All data is stored locally in ~/.mcp-toolselect/:

tool-registry.json - Registered tools and their metadata
tool-stats.json - Aggregated usage statistics
usage-log.jsonl - Append-only usage log for auditing

@aegis-ai/mcp-toolselect