Agent Capability Matcher

Agent Capability Matcher MCP Connector for Claude

A+

Optimizes task assignment to agents using capability matching and load balancing.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides deterministic bipartite matching to connect tasks with the most suitable agents. It uses strategies like greedy, hungarian, and priority-weighted to ensure tasks are assigned to agents possessing all required capabilities while maintaining optimal workload distribution. Use match_tasks to perform assignments, validate_system_health to monitor unassigned ratios and agent loads, and get_capability_stats to analyze skill coverage across your agent pool.

matchingload-balancingbipartiteschedulingcapabilities

3 tools expose this connector's capabilities to your AI agent.

get_capability_stats

Provides a summary of capability coverage across the entire agent pool

match_tasks

Executes the core matching logic to pair tasks with agents based on a specific strategy

validate_system_health

Evaluates the quality of the matching result by checking against predefined operational thresholds

See how to talk to your AI agent using Agent Capability Matcher.

Match these tasks to the available agents using the greedy strategy.

Task 'Data Analysis' has been assigned to Agent 'Alpha' with a match score of 0.85.

Check the current health of the matching system.

The system is healthy. Unassigned task ratio is 5% and all agent loads are below 95%.

Show me the capability distribution for all agents.

There are 12 unique capabilities. 'Python' is possessed by 8 agents, and 'Data Science' is possessed by 5 agents.

The system uses the `match_tasks` tool to pair tasks with agents. An agent is only eligible if they possess every capability required by the task. The match score is then calculated based on capability alignment and current agent load.

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