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Metis

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Metis · Memory Curator — Recall

recall

Search all memory layers in one call to find ranked notes, findings, decisions, and knowledge with source attribution. Filter by scope, agent, project, or layer to narrow results.

Instructions

Search across ALL memory layers in one call.

The unified front door to everything Metis remembers — personal notes,
agent findings, project decisions, PDF knowledge, ideas, and session
history. Returns ranked results with source attribution.

Searches up to 6 layers and merges results using Reciprocal Rank Fusion:
  1. Memory palace (memory_entries — keyword)
  2. Episodic memory (events — vector + keyword)
  3. Semantic memory (concepts — vector + keyword)
  4. Procedural memory (workflows — vector + keyword)
  5. Knowledge databases (PDF chunks — vector)
  6. Ideas (ideas table — keyword)

Use scope/agent_id/project_id to narrow results:
  recall("HAT diagnostics")                          → everything
  recall("search patterns", agent_id="librarian")    → librarian's wisdom
  recall("elimination", project_id="article-1")      → Article 1 decisions
  recall("mixed models", scope="global")             → cross-cutting knowledge

Args:
    query: Natural language search query.
    scope: Filter by scope: 'global', 'agent', 'project', 'session'.
        Empty string (default) searches all scopes.
    agent_id: Filter to memories from a specific agent (e.g. 'librarian').
    project_id: Filter to memories linked to a specific project.
    layers: Comma-separated layers to search. Options: 'memory', 'episodic',
        'semantic', 'procedural', 'knowledge', 'ideas', or 'all' (default).
    top_k: Number of results to return (default 10).
    recency_half_life: Days until recency boost halves (default 90). Set to 0
        to disable recency weighting. A 90-day-old entry gets ~37% of a
        fresh entry's time bonus; 180-day ~14%.

Returns:
    Ranked results from across all matching layers, each with its source
    layer, relevance score, timestamp, and content preview.

Input Schema

NameRequiredDescriptionDefault
queryYes
scopeNo
top_kNo
layersNoall
agent_idNo
project_idNo
recency_half_lifeNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.4

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description fully covers behavioral traits: it searches 6 memory layers, uses Reciprocal Rank Fusion, returns ranked results with source attribution, and explains recency weighting with a half-life parameter. There is no destructive action implied, and the merging mechanism is transparent.

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 a summary line, explanation, examples, and parameter list. It is longer than minimal but every sentence adds value; the examples and parameter details are necessary for clarity. Slightly less than perfect conciseness due to length.

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

Completeness5/5

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

Given the tool's complexity (7 parameters, no annotations, an output schema exists but description still covers return values), the description is complete. It explains all parameters, behavior, merging strategy, and return format, leaving no critical 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 input schema has 0% description coverage, but the description compensates fully by detailing each parameter: query, scope, agent_id, project_id, layers, top_k, and recency_half_life, including default values and examples. This provides rich semantic meaning beyond the bare schema.

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

Purpose5/5

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

The description immediately states 'Search across ALL memory layers in one call' with a clear verb and resource. It distinguishes itself from sibling search tools (e.g., search_memory, search_session_memory) by positioning as the unified front door that covers 6 specific layers, making its purpose unmistakable.

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

Usage Guidelines4/5

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

The description provides explicit usage examples showing how to narrow results using scope, agent_id, and project_id, and implies broad vs. narrowed use. It does not explicitly mention when not to use it or list alternative tools, but the context is clear enough for an agent to decide.

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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