@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

Or clone and run directly:

Configuration

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

Or if running from source:

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:

Response:

register_tool

Register a tool with its capabilities so it can be recommended for future tasks.

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:

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:

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:

list_tools

List all registered tools grouped by category.

Parameters:

• category (string, optional) - Filter by category

How It Works

1. Register tools with their capabilities and use cases

2. Ask for recommendations by describing your task

3. Record outcomes after using a tool (success/failure)

4. 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

License

MIT - Copyright 2026 AEGIS AI Cooperative