mcp-bench
MCP-Bench is an evaluation framework that benchmarks tool-using LLM agents on complex tasks using the Model Context Protocol (MCP). It orchestrates discovery/connection to multiple MCP servers, runs benchmark tasks (single- and multi-server settings), and evaluates outputs (including LLM-as-judge).
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Strengths: uses environment variables and a dedicated api_key file for third-party integrations (reduces risk of hardcoding in code). Risks/unknowns: secret-handling practices (e.g., whether keys/tokens are redacted from logs) are not confirmed in the provided excerpt; multiple third-party MCP servers expand the trust boundary and make it harder to ensure uniform secure logging and request handling; no evidence of least-privilege scopes for tokens.
⚡ Reliability
Best When
You want repeatable, local benchmarking of MCP tool-using agents across many heterogeneous tool servers with configurable model providers and judge-based scoring.
Avoid When
You cannot safely provide and manage multiple third-party API keys or you need a minimal dependency surface; also avoid when you require strong guarantees that tool calls and logs won’t expose sensitive data.
Use Cases
- • Benchmarking LLM tool-use and planning ability across MCP tool ecosystems
- • Comparing model providers on consistent end-to-end agent workflows
- • Evaluating multi-tool, multi-server task completion using real-world external tools
- • Generating and running benchmark task suites for MCP-based agents
Not For
- • Production deployment of an agent for end-user workflows
- • A hosted SaaS API for executing tasks (it is primarily a local/runnable framework)
- • A security gateway or access-control layer for MCP tools
Interface
Authentication
Authentication is handled via API keys loaded into environment variables; multiple third-party services may require their own keys.
Pricing
No pricing for the benchmark itself is described; compute and LLM/API usage costs apply to the run.
Agent Metadata
Known Gotchas
- ⚠ Hard-coded judge model requirement for reproducing results (o4-mini mentioned) can surprise automated harnesses.
- ⚠ Benchmark relies on many external MCP servers (28 listed); failures may be tool/provider-specific rather than framework-specific.
- ⚠ Sensitive API keys are required for several MCP servers; misconfiguration can lead to partial server availability.
- ⚠ Multi-server task settings increase complexity of tool routing and failure modes.
Alternatives
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Scores are editorial opinions as of 2026-03-30.