MCP Hub
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MODAL_ID | No | Your Modal ID for code execution | |
| LLM_PROVIDER | No | Your chosen LLM provider | nebius |
| NEBIUS_API_KEY | No | Your Nebius API key | |
| OPENAI_API_KEY | No | Your OpenAI API key | |
| TAVILY_API_KEY | No | Your Tavily API key for web search | |
| ANTHROPIC_API_KEY | No | Your Anthropic API key | |
| MODEL_SECRET_TOKEN | No | Your Modal secret token | |
| HUGGINGFACE_API_KEY | No | Your HuggingFace Inference API key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ShallowCodeResearch_agent_research_requestC | This function researches a coding request from the user, generates code, executes it, and returns a clean summary of the results. This is an mcp server function that responds to research coding requests from users. Returns: A tuple containing the JSON result from the orchestrator and a clean summary |
| ShallowCodeResearch_agent_question_enhancerC | Wrapper for QuestionEnhancerAgent to provide question enhancement. Returns: Enhanced question result with sub-questions |
| ShallowCodeResearch_agent_web_searchC | Wrapper for WebSearchAgent to perform web searches. Returns: Web search results with summaries and URLs |
| ShallowCodeResearch_agent_llm_processorC | Wrapper for LLMProcessorAgent to process text with LLM. Returns: LLM processing result with output and metadata |
| ShallowCodeResearch_agent_citation_formatterC | Wrapper for CitationFormatterAgent to format citations. Returns: Formatted citations result with APA-style references |
| ShallowCodeResearch_agent_code_generatorC | Wrapper for CodeGeneratorAgent to generate Python code. Returns: A tuple containing the generation result and raw code |
| ShallowCodeResearch_code_runner_wrapperB | Wrapper for CodeRunnerAgent that uses async execution with warm pool. Ensures a sandbox is spawned if not already present, waits for readiness, and then executes the code. Provides user-friendly error messages. Returns: The execution result or user-friendly error message |
| ShallowCodeResearch_get_health_statusB | Get comprehensive system health status including advanced monitoring features. Retrieves detailed health information about the system including availability of advanced features, system resources, and operational metrics. Returns basic information if advanced monitoring is not available. Returns: A dictionary containing system health status and metrics |
| ShallowCodeResearch_get_performance_metricsB | Get performance metrics and analytics for the MCP Hub system. Collects and returns performance metrics including execution times, success rates, error counts, and resource utilization. Provides basic information if advanced metrics collection is not available. Returns: A dictionary containing performance metrics and statistics |
| ShallowCodeResearch_get_cache_statusC | Get cache status and statistics. |
| ShallowCodeResearch_get_sandbox_pool_status_syncC | Synchronous wrapper for sandbox pool status. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 11 tools
Multiple tools have overlapping or unclear boundaries. For example, ShallowCodeResearch_agent_research_request appears to combine research, code generation, and execution, which overlaps with ShallowCodeResearch_agent_code_generator and ShallowCodeResearch_code_runner_wrapper. The distinction between ShallowCodeResearch_agent_llm_processor and ShallowCodeResearch_agent_question_enhancer is also vague, as both involve LLM-based text processing. This ambiguity could lead to agent misselection.
The naming follows a mostly consistent pattern with a prefix 'ShallowCodeResearch_' and snake_case throughout. However, there are minor deviations: ShallowCodeResearch_agent_research_request uses 'research_request' while others use more specific terms like 'code_generator', and ShallowCodeResearch_get_sandbox_pool_status_sync includes 'sync' as a suffix, which is not present in other getter tools. Overall, the naming is readable and largely predictable.
With 11 tools, the count is reasonable for a server focused on code research and system monitoring. It covers a range of functions from code generation and execution to health checks, which aligns with the apparent scope of an MCP Hub. While not perfectly scoped (some tools could be consolidated), it does not feel overly heavy or thin for the domain.
The tool surface has notable gaps in coverage. For a code research domain, there are tools for generation, execution, and monitoring, but missing operations like code editing, debugging, or version control integration. The set includes getters for status and metrics but lacks corresponding setters or configuration tools. Agents may encounter dead ends when trying to perform comprehensive coding workflows beyond basic generation and execution.