Loading ToolBench
A Model Context Protocol server supporting xAI's Grok models with filesystem and research tools
This server defines 29 tools with significant quality gaps across naming, descriptions, and schema completeness. Many tools have adequate names (verb_noun pattern: answer_query_direct, read_file_content, execute_terminal_command) but descriptions are often generic or incomplete. Parameter schemas are present for most tools but lack depth in field-level descriptions. Critical gaps include missing input schemas for 3 tools (explain_topic_with_docs, get_doc_snippets, generate_project_guidelines show .js files with no visible schema registration), no documented output schemas for any tool, and missing parameter descriptions for several complex inputs. Error handling is minimal, no recovery guidance, no actionable error messages, and no clear distinction between retryable vs fatal errors. The server conflates the xAI API into LLM-based tools without proper scoping or permission declarations.
Answers a natural language query using only the internal knowledge of the configured xAI model (${modelId}). Does not use Live Search. Requires a 'query' string.
Answers a natural language query using the configured xAI model (${modelId}) enhanced with Live Search results for up-to-date information. Requires a 'query' string.
Suggests architecture patterns for specific use cases based on industry best practices. Provides implementation examples and considerations for the recommended patterns. Includes diagrams and explanations of pattern benefits and tradeoffs. Uses the configured xAI model (${modelId}) with Live Search. Requires 'requirements' and 'tech_stack'.
Analyzes code snippets by comparing them with best practices from official documentation found via Live Search. Identifies potential bugs, performance issues, and security vulnerabilities. Uses the configured xAI model (${modelId}) with Live Search. Requires 'code', 'language', and 'analysis_focus'.
Analyzes database schema design for best practices, normalization, and performance.
Three tools (explain_topic_with_docs, get_doc_snippets, generate_project_guidelines) reference .js files with NO visible input schemas in source. Cannot verify parameter types or constraints.
NO tool documents its output schema. LLMs cannot plan downstream tool calls or extract relevant fields. Agents cannot compose tools effectively without knowing return structure.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 61 | 2026-07-28+ | v2 |
Scans project dependencies for known security vulnerabilities using web search.
Generates comprehensive documentation for software projects.
Edits specific sections of a file using find-and-replace operations.
Executes a terminal command in the workspace.
Explains a technical topic using official documentation found via Live Search, including examples and best practices.
Generates comprehensive project guidelines and best practices based on project requirements and tech stack.
Gets a tree view of a directory and its contents.
Retrieves relevant documentation snippets for a specified topic using Live Search.
Gets detailed information about a file or directory.
Lists the contents of a directory.
Assists in designing microservice architectures with service boundaries and communication patterns.
Moves or renames a file or directory.
Reads the contents of one or more files from the workspace.
Provides guidance on regulatory compliance requirements for applications.
Answers a query using internal knowledge and saves the result to a file.
Answers a query using web search and saves the result to a file.
Retrieves documentation snippets and saves them to a file.
Generates and saves project guidelines to a file.
Explains a topic with documentation and saves to a file.
Searches for files matching a pattern.
Provides security best practices and recommendations for a given application context.
Compares multiple technologies, approaches, or solutions based on web search results and analysis.
Generates comprehensive testing strategies based on application type and requirements.
Writes content to one or more files in the workspace.
execute_terminal_command tool accepts arbitrary shell commands with only a timeout parameter. No input validation, no allowlist, no path traversal prevention within command execution itself. This is a catastrophic security risk for an agent, allows arbitrary code execution.
write_file_content, edit_file_content, move_file_or_directory, and execute_terminal_command are destructive/reversible operations. Descriptions do NOT indicate they modify state or trigger side effects. Agents cannot reason about consequences.
13 tools for AI-driven advice (code_analysis_with_docs, technical_comparison, security_best_practices_advisor, etc.) lack error handling guidance. No recovery instructions if xAI API fails. No timeout handling. No indication of retryability.
save_* tools (save_generate_project_guidelines, save_doc_snippet, etc.) do NOT document which xAI model is used or how the save operation behaves if the file already exists. No overwrite vs append guidance.
dependency_vulnerability_scan, technical_comparison, explain_topic_with_docs, and similar tools rely on 'Live Search' but do NOT document whether search is live or cached, how results are ranked, or how many results are returned. Could return thousands of results, exhausting context.
No tools declare permissions (read:file, write:file, execute:command, etc.). Agents cannot determine what authority is required. No audit trail capability or permission-gating mechanism visible.
5 tools with compound operations (save_* tools) name violates single-responsibility. 'save_answer_query_websearch' = call xAI + write file. Should be two separate tools: answer_query_websearch (already exists) + write_file_content (already exists).
architecture_pattern_recommendation, microservice_design_assistant, and similar advisory tools accept complex nested objects (requirements with description, scale, key_concerns) but do NOT document expected string lengths, enum values for 'scale', or what constitutes a valid 'key_concern'. LLMs will hallucinate values.