Loading ToolBench
MCP server providing tools for GraalVM and Truffle performance analysis, benchmarking, profiling, and documentation access
Server has 18 tools with basic descriptions and input schemas, but significant quality gaps. Tool names lack consistent verb-noun structure (e.g., 'give_task_to_graal_command_subagent' is awkward; 'ask_recommended_commands' is vague). Descriptions are present but often verbose and lack actionable guidance for LLM selection. Input schemas visible in tool_register.py show parameter types and descriptions, but many descriptions are generic or incomplete. No output schemas documented. Error handling guidance absent. Benchmark tools (11 of 18) are repetitive variants that should be consolidated. No tool annotations (readOnlyHint, destructiveHint) despite clear risk levels. Composition is poor, tools do not chain well (no IDs returned to enable downstream calls).
Get intelligent GraalVM CLI command suggestions based on natural language queries using semantic routing.
Convert a GraalVM .bgv compiler graph file to .json format for easier analysis and visualization. It's recommended to save the output to a json file and query it with jq.
Fetch a specific GraalVM documentation resource by its URI. Provide the URI (e.g., 'file://graal/') to retrieve the corresponding document as embedded resource its content, MIME type, and metadata. Use this to access detailed documentation content after discovering available resources using the list_graal_documentation tool.
Give a task to the GraalVM and Truffle command sub-agent The agent is knows for each GraalVM command: - the the command execution & options, - the functional description, - the output format, - which insights you can extract from the command output, - correlation with other tools and commands, Possible tasks are: - "Generate GraalVM CLI commands to achieve a specific optimization or profiling goal." - "Explain the purpose and usage of specific GraalVM commands." - "Explain the output of a specific GraalVM command and how to interpret it."
11 benchmark tools are near-identical variants (run_benchmark_with_*). This violates single-responsibility and composition patterns. LLM must reason between 11 similar names instead of one parameterized tool. Consolidate into run_benchmark(harness_script, benchmark, profiling_mode: enum[cpu_sampler|cpu_tracer|memory_tracer|...]).
Tool names lack consistent verb-noun structure. 'give_task_to_graal_command_subagent' is awkward and unclear; 'ask_recommended_commands' is vague (ask whom?). Rename to: delegate_to_graal_agent, get_command_suggestions. LLMs parse intent from names, clarity matters.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 44 | 2026-07-28+ | v2 |
Display essential MCP server information including version, execution environment, and available tools.
List all available GraalVM documentation resources. Returns a list of resources with their MCP resource URIs (format: file://graal/..., resource://graal/...), names, and descriptions. These URIs can be used to fetch the actual resource content from the MCP server using the MCP resource reading protocol (not web URLs). Use this to discover available documentation before fetching specific resources.
Execute a benchmark using the configured Truffle language interpreter with specified parameters.
Collect and analyze compilation statistics during benchmark execution to understand JIT compiler performance metrics.
Execute benchmark with CPU sampling to identify performance hotspots and execution time distribution across functions.
Execute benchmark with CPU tracing for detailed execution timeline and performance event collection.
Execute benchmark with compiler graph dumping (BGV format) for IR visualization and optimization phase analysis.
Execute benchmark with memory tracing to track allocations and identify memory bottlenecks.
Execute benchmark with performance warning collection to identify compiler-level optimization barriers.
Execute benchmark with specialization statistics collection to analyze polymorphic behavior and code versions.
Execute benchmark with compilation tracing to understand which methods are compiled and when.
Execute benchmark with inlining tracing to analyze inlining decisions and missed optimization opportunities.
Execute benchmark with transfer-to-interpreter tracing to identify deoptimization and code cache invalidation events.
Search GraalVM documentation using semantic search to find relevant resources and information.
No output schemas documented. Tools return results but LLMs cannot plan downstream calls without knowing field names and types. Document return types for all tools, especially list_graal_documentation and search_documentation (what fields do results contain?).
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk levels. bgv2json, run_benchmark_* are WRITE operations; get_*, list_*, search_* are READ_ONLY. Add annotations to enable agent safety policies.
Error handling absent. No guidance on retryability, user-fixable errors, or recovery steps. If a benchmark fails, what should the LLM do? Add error classification and recovery hints to all tool descriptions.