MIF Tools presents a specialized memory conversion utility with clear, descriptive tool names and well-structured input schemas. All 5 tools are explicitly registered with descriptions and type-annotated parameters. However, descriptions lack depth regarding when/why to use each tool over alternatives, and output schemas are not formally documented. Error handling is minimal, validation failures and format mismatches return generic JSON without recovery guidance. The tool set follows good verb-noun naming (export_, import_, validate_, list_, inspect_) and parameter descriptions are present, but lack constraint documentation (valid format strings, limits, ranges). Overall fair execution of core patterns with meaningful gaps in LLM-guidance and error classification.
Tools (5)
export_memoriesread onlysource verified79/100
Convert memories from any supported format to MIF v2 JSON.
import_memoriesread onlysource verified78/100
Convert MIF v2 JSON to a target format.
inspect_memoriesread onlysource verified76/100
Show a summary of a memory file.
list_formatsread onlysource verified72/100
List all available memory format adapters.
validate_memoriesread onlysource verified75/100
Validate a MIF JSON document against the schema and run semantic checks.
Output schemas not formally documented. While tools return JSON objects (e.g., validate_memories returns {valid, schema_errors, semantic_valid, semantic_warnings, summary}), the response structure is embedded in code comments and return statements. LLMs cannot discover what fields to expect without reading the implementation.
Parameter descriptions lack constraint documentation. 'from_format' parameter states 'Options: shodh, mem0, crewai, langchain, generic, markdown' but only in prose. Should be a formal enum in the schema with each value documented. Without enum constraints, LLMs may hallucinate invalid format IDs.
Tool descriptions lack LLM-guidance on selection and usage. Descriptions state WHAT the tool does but not WHEN to call it or how it differs from similar tools. E.g., export_memories and import_memories are inverse operations, but this relationship is not stated. Agents may struggle to choose the right direction.
Recommendations
Add formal enum definitions to schema for 'from_format' and 'to_format' parameters. Replace prose option lists with JSON Schema enum constraints. Include a description for each valid value (e.g., 'shodh: MIF v2 native format, used by Shodh agents').
Document output schemas for all tools. For validate_memories, formalize the response as {valid: boolean, schema_valid?: boolean, semantic_valid?: boolean, schema_errors?: string[], semantic_warnings?: string[], summary?: {memories: number, has_graph: boolean, has_extensions: boolean}}.
Enhance tool descriptions with usage context and differentiation. Example for export_memories: 'Convert memories from any supported format (mem0, CrewAI, LangChain, etc.) to MIF v2 JSON. Use this to unify memories from multiple agent frameworks before validation or analysis. If you have raw memory data in an unknown format, try import_memories with from_format omitted for auto-detection.'
Implement structured error responses for all tools. Return errors as {error: string, error_type: 'validation_error'|'format_unsupported'|'malformed_input'|'internal_error', suggestion: string, retry: boolean}. Example: {error: 'Invalid format ID: xyz', error_type: 'format_unsupported', suggestion: 'Use one of: shodh, mem0, crewai, langchain, generic, markdown', retry: false}.
Add input validation with clear error messages. Catch load() and dump() exceptions and return structured errors instead of stack traces. Example: 'if format not in ["shodh", "mem0", ...]: return {error: "Format not supported"}.'
Error responses lack recovery guidance and classification. When validate_memories detects schema errors, it returns {valid: false, schema_errors: []} with no actionable next steps. No indication of whether the error is user-fixable (e.g., 'Invalid memory_type: expected one of […]'), retryable, or fatal.
No input validation or sanitization visible in tool implementations. If an LLM passes malformed JSON or a non-existent format ID, the code calls load(data, format=...) without explicit error handling shown. Stack traces or generic exceptions would reach the LLM without actionable guidance.
Auto-detection behavior not documented. export_memories and inspect_memories accept from_format=None with auto-detection, but the detection order and confidence are not documented. If detection fails or is ambiguous, the user has no recourse.
export_memoriesinspect_memories
Document auto-detection behavior in tool descriptions. State: 'from_format is optional. If omitted, format is auto-detected in this order: Shodh (MIF v2/v1), mem0 JSON, Generic JSON, Markdown. Auto-detection may fail on ambiguous inputs, provide from_format explicitly if detection is uncertain.'
Add pagination/limits documentation. If list_formats or inspect_memories can return very large lists, document result limits and offer cursors. Example: 'Returns up to 1000 memory records. Use offset/limit for pagination.'
Consider adding a tool for multi-step conversions or batch operations. E.g., convert_batch(memories: [{data, from_format}], to_format) to enable agents to process multiple memory sets in one call, reducing latency and token overhead.
Add parameter descriptions for edge cases. E.g., for to_format in import_memories: 'Target format ID. Options: shodh, mem0, crewai, langchain, generic, markdown. Default: shodh (MIF v2 native). If you do not specify a format, output is returned as MIF v2 JSON.'