Loading ToolBench
MCP server for vLLM - expose vLLM capabilities to AI assistants
Moderate quality. Tool naming follows verb_noun convention well (vllm_chat, vllm_complete, list_models, get_model_info, start_vllm, stop_vllm). Descriptions are present and adequate (average ~80-120 chars, within acceptable range). Input schemas are properly defined with type information and nested objects for complex parameters (messages array with role/content objects). However, critical gaps exist: (1) Output schemas are NOT documented anywhere, tools list only input schemas; (2) Error handling descriptions are completely absent, no guidance on retryability, failure modes, or recovery steps; (3) No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite having tools with clear side effects (start_vllm is IRREVERSIBLE, stop_vllm and restart_vllm are REVERSIBLE); (4) Parameters like 'container_name' in stop_vllm lack constraints and validation hints; (5) No pagination guidance for list_* tools despite returning model arrays; (6) get_vllm_logs 'follow' parameter (streaming logs) lacks description of streaming behavior and timeout implications. The tool definitions are functional but lack production-grade polish and agent-optimization patterns.
Get detailed information about a specific model
Get detailed platform status including container runtime, GPU availability, and system information
Get logs from a vLLM container to check for errors or debug issues
List all available models on the vLLM server
List all vLLM Docker containers
Restart a vLLM Docker container
Start a vLLM server in a Docker container. Automatically detects platform (Linux/macOS/Windows) and GPU availability.
NO OUTPUT SCHEMAS DOCUMENTED. All 12 tools list only input schemas. LLMs cannot know what fields to expect in responses, cannot plan downstream tool calls, and cannot extract critical IDs for chaining. This is a critical gap in tool composition and agent planning capability.
NO TOOL ANNOTATIONS despite having tools with clear side effects. start_vllm is marked IRREVERSIBLE but has no destructiveHint annotation. stop_vllm removes containers but has no destructiveHint. restart_vllm is idempotent but has no idempotentHint. These annotations are critical for agent planning and safety, LLMs use them to reason about retry safety and irreversibility.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 52 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 30 | - | v1 |
Stop a running vLLM Docker container
Run a benchmark against the vLLM server using GuideLLM to measure throughput, latency, and other performance metrics
Send a chat message to the vLLM server. Supports multi-turn conversations.
Generate text completion using vLLM. Good for code completion and text generation.
Check the health and status of the vLLM server
ERROR HANDLING ABSENT. No tool description explains what happens on failure, how to recover, or whether errors are retryable. Example: vllm_chat has no guidance on model timeout, context length exceeded, or service unavailable. start_vllm has no error message if Docker is not installed. This forces LLMs to guess at recovery strategies.
PARAMETER CONSTRAINTS MISSING. Parameters like 'gpu_memory_utilization' accept 0-1 but schema lacks min/max constraints. 'lines' in get_vllm_logs has no upper bound (could request 1M lines, blowing context window). 'timeout' in stop_vllm is unbounded. Model 'temperature' accepts 0-2 per description but schema lacks numeric constraints. Unbounded parameters let LLMs pass absurd values.
DANGEROUS DEFAULT VALUE: stop_vllm 'remove' parameter defaults to true, meaning containers are removed by default. Per pattern:default-values, destructive defaults should be false to prevent accidental deletion. Should require explicit remove=true.
STREAMING BEHAVIOR UNDOCUMENTED. get_vllm_logs 'follow' parameter (true for streaming) lacks description of streaming semantics, does it block indefinitely? What's the timeout? How does the agent handle streamed output in a stateless context? The parameter description is missing entirely.
AMBIGUOUS PARAMETER FORMAT. vllm_benchmark 'rate' parameter accepts both numeric values and the string 'sweep', but this is not formalized in schema, no enum constraint. 'data' parameter accepts 'emulated' or 'path' but no enum definition. LLMs will guess at valid formats.
PARAMETER RELATIONSHIPS UNDOCUMENTED. In vllm_chat and vllm_complete, 'model' is optional with default behavior described only as 'uses default'. What IS the default? Can you mix models in multi-turn conversation? These interdependencies must be explicit per pattern.
NO PAGINATION GUIDANCE for list_* tools. list_models and list_vllm_containers may return unbounded lists, but no limit, offset, or cursor parameters exist. Per pattern:paginated-result, result lists should accept pagination to prevent context window exhaustion.
TOOL DISCOVERY GAPS. Relationship between list_models and get_model_info not documented, when should agent call which? Relationship between vllm_status and get_platform_status unclear. Relationship between start_vllm and vllm_status (how to verify startup succeeded?) not guided.