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MCP server for searching Leeroopedia's curated ML/AI knowledge base. Provides 8 agentic tools for knowledge base search, planning, review, verification, diagnosis, hypothesis generation, and hyperparameter lookup.
Leeroopedia MCP provides 8 well-named agentic tools with clear, detailed descriptions and proper input schemas. All tools follow verb_noun naming convention (search_knowledge, build_plan, review_plan, verify_code_math, diagnose_failure, propose_hypothesis, query_hyperparameter_priors, get_page). Descriptions are comprehensive (averaging ~280 characters), explaining WHAT the tool does, WHEN to use it, and what it returns. Input schemas use proper JSON Schema with type definitions and parameter descriptions. However, there are notable gaps: no output schemas are documented, no error handling guidance is provided, and no tool annotations (readOnlyHint/destructiveHint) are present despite all tools being READ_ONLY. The server lacks pagination patterns for tools returning lists, and does not include recovery guidance for failure modes. These are production-grade agentic tools but fall short of A-tier polish.
Get a structured implementation plan based on knowledge base documentation. Covers a wide range of ML/AI frameworks, libraries, and tools with architecture docs, implementation patterns, configuration references, and troubleshooting guides. Returns an actionable plan with numbered steps, specs, and validation criteria — all based on how the framework actually works. Use this tool when you: - Are about to implement something and want the correct sequence of steps - Need a plan informed by real framework documentation, not just general knowledge - Want validation criteria to verify your implementation against Returns: overview, key specs, numbered steps, and validation criteria.
Diagnose errors, failures, or unexpected behavior using knowledge base documentation. Checks your symptoms and logs against known failure patterns, common misconfigurations, and documented environment issues — finding root causes faster. Use this tool when you need: - Root cause analysis of errors, crashes, or unexpected behavior - Debugging configuration issues or dependency problems - Understanding why a framework behaves differently than expected Returns: diagnosis, fix steps, and prevention advice.
Retrieve the full content of a specific knowledge base page by its exact ID. Other tools return [PageID] citations in their responses. If you need more detail from a cited page, call this tool with that page ID to get the full content.
Propose ranked approaches or solutions based on knowledge base documentation. When you're unsure how to proceed, this tool suggests alternative approaches ranked by fit — all backed by documented framework patterns and best practices. Use this tool when you need: - Ideas for how to implement or architect something - Alternative approaches ranked by fit for your use case - Suggestions backed by documented framework patterns and best practices Returns: ranked hypotheses with rationale and suggested next steps.
No output schemas documented. Tools return synthesized answers, structured plans, diagnoses, and ranked hypotheses, but the response format (fields, types, pagination, citation structure) is not formally defined. This forces LLMs to guess what fields exist and parse unstructured text.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite all tools being explicitly READ_ONLY. Annotations would signal to clients that these tools are safe to retry and do not modify state.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 68 | <=2025-11-25 | v2 |
| 2026-03-09 | C | 67 | - | v1 |
Query documented configuration values, recommended ranges, and tuning heuristics. Look up recommended parameter values, default settings, and tuning strategies for frameworks and libraries — based on documented best practices. Use this tool when you need: - Default or recommended values for framework configuration parameters - Recommended ranges and tuning strategies for any setting - Context-specific suggestions (hardware, model size, task type, scale) Returns: suggestion table with ranges and KB-grounded justification.
Review your implementation plan against knowledge base documentation before coding. Pass your proposed approach and the KB will check it against documented best practices, known pitfalls, and real framework behavior — catching mistakes before you write code. Use this tool when you: - Have a plan and want to validate it before executing - Want to catch incorrect assumptions about how a framework works - Need to know what pitfalls or edge cases to watch out for Returns: approvals (what looks good), risks, and improvement suggestions.
Search the knowledge base for framework documentation, API references, config formats, and best practices. Covers a wide range of ML/AI frameworks, libraries, and tools with architecture docs, implementation patterns, configuration references, and troubleshooting guides. Use this tool when you need to: - Understand how a framework, library, or API works before implementing - Look up config formats, data structures, or expected behavior - Learn about architecture, design patterns, or conventions of a project - Get verified information instead of guessing about framework internals IMPORTANT: Use this tool BEFORE you start coding whenever the task involves a framework or library. It is much faster and more accurate than guessing. Call this tool multiple times in parallel with different queries to search from multiple angles at once. Returns a synthesized answer with [PageID] citations.
Verify code correctness against knowledge base documentation and reference implementations. Checks your code against documented behavior, reference implementations, and API contracts — catching errors before they become bugs. Use this tool when you need: - Verification that code correctly implements a concept, algorithm, or API contract - Detection of logic errors, off-by-one mistakes, or wrong assumptions - Comparison against reference implementations or documented behavior Returns: verdict (Pass/Fail), analysis of discrepancies.
No error handling guidance. Tools that search and synthesize from a knowledge base will encounter cases where no relevant documentation exists, disambiguation is needed, or the KB is unreachable. No recovery hints (e.g. 'Try a broader query' or 'Check KB status') are provided.
search_knowledge and query_hyperparameter_priors lack pagination parameters. Tools that return synthesized answers from a KB may produce long lists (e.g. multiple framework options, many parameter recommendations). No limit, page, or offset parameters constrain results or enable pagination.
Missing dependency hints. Tools like build_plan and verify_code_math reference other tools (search_knowledge for initial research, review_plan before verify_code_math) but do not explicitly guide the LLM to call them first. Description should include 'Call search_knowledge first to understand the framework before using build_plan.'