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memory_list

Retrieve stored memories from Memora using text search, date ranges, tags, and metadata filters to organize and access information efficiently.

Instructions

List memories, optionally filtering by substring query or metadata.

Args: query: Optional text search query metadata_filters: Optional metadata filters limit: Maximum number of results to return (default: unlimited) offset: Number of results to skip (default: 0) date_from: Optional date filter (ISO format or relative like "7d", "1m", "1y") date_to: Optional date filter (ISO format or relative like "7d", "1m", "1y") tags_any: Match memories with ANY of these tags (OR logic) tags_all: Match memories with ALL of these tags (AND logic) tags_none: Exclude memories with ANY of these tags (NOT logic) sort_by_importance: Sort results by importance score (default: False, sorts by date)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
metadata_filtersNo
limitNo
offsetNo
date_fromNo
date_toNo
tags_anyNo
tags_allNo
tags_noneNo
sort_by_importanceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The implementation of the MCP tool 'memory_list' which wraps the internal _list_memories function.
    async def memory_list(
        query: Optional[str] = None,
        metadata_filters: Optional[Dict[str, Any]] = None,
        limit: Optional[int] = None,
        offset: Optional[int] = 0,
        date_from: Optional[str] = None,
        date_to: Optional[str] = None,
        tags_any: Optional[List[str]] = None,
        tags_all: Optional[List[str]] = None,
        tags_none: Optional[List[str]] = None,
        sort_by_importance: bool = False,
    ) -> Dict[str, Any]:
        """List memories, optionally filtering by substring query or metadata.
    
        Args:
            query: Optional text search query
            metadata_filters: Optional metadata filters
            limit: Maximum number of results to return (default: unlimited)
            offset: Number of results to skip (default: 0)
            date_from: Optional date filter (ISO format or relative like "7d", "1m", "1y")
            date_to: Optional date filter (ISO format or relative like "7d", "1m", "1y")
            tags_any: Match memories with ANY of these tags (OR logic)
            tags_all: Match memories with ALL of these tags (AND logic)
            tags_none: Exclude memories with ANY of these tags (NOT logic)
            sort_by_importance: Sort results by importance score (default: False, sorts by date)
        """
        try:
            items = _list_memories(
                query, metadata_filters, limit, offset,
                date_from, date_to, tags_any, tags_all, tags_none,
                sort_by_importance,
            )
        except ValueError as exc:
            return {"error": "invalid_filters", "message": str(exc)}
        return {"count": len(items), "memories": items}
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It adds valuable behavioral context including date format examples ('7d', '1m'), tag logic (OR/AND/NOT), and sorting defaults. However, it omits critical traits like confirming the read-only nature, rate limits, or how pagination behaves with 'unlimited' defaults.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with the purpose front-loaded in the first sentence, followed by a clear Args section. While the Args block is lengthy, every line is necessary given the schema coverage gap. Minor redundancy ('Optional' repeated) prevents a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While parameters are thoroughly documented and an output schema exists (reducing the need to describe returns), the description fails to address the complex sibling landscape. For a tool with 10 parameters and numerous alternative search/list methods, guidance on the specific use case (substring vs. semantic) is missing, leaving significant contextual gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage (titles only), but the description comprehensively compensates by documenting all 10 parameters with precise semantics, default values, and format examples (e.g., ISO or relative dates). This is exemplary parameter documentation via description text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists memories with optional filtering by substring or metadata, providing a specific verb and resource. However, it fails to distinguish from siblings like `memory_semantic_search` or `memory_list_compact`, leaving ambiguity about which listing tool to use.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this substring-based search versus vector-based alternatives (`memory_semantic_search`, `memory_hybrid_search`), nor any mention of prerequisites or pagination limits. The agent receives no help selecting this tool over its many siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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