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User memory search

memory_search
Read-only

Search the authenticated user's stored long-term memories (preferences, facts, context) using semantic similarity.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "memory_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of memories to return (1-50).
queryYesNatural-language query describing the memory to look up.
categoryNoOptional category filter (e.g. preferences, work, personal).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds two useful behavioral facts — results are scoped to the authenticated user, and matching is semantic rather than keyword — but says nothing about result ordering, empty-result behavior, or scoring, which matters for a ranked retrieval tool.

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

Conciseness5/5

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

A single front-loaded sentence that identifies the actor, the resource, and the retrieval method with no filler. Nothing is repeated from the schema or annotations.

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

Completeness4/5

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

For a three-parameter read-only search with full schema coverage, no output schema, and safety already covered by annotations, the description is close to sufficient. The remaining gap is routing: it never states how this differs from rag_search or rag_similar, which is the main thing an agent still needs.

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

Parameters3/5

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

Schema description coverage is 100%, so query, limit, and category are all self-documenting, and the baseline is 3. The description adds only the notion of semantic matching, not any parameter-level detail such as how category values interact with the semantic query or how limit affects ranking.

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?

States a specific verb ('Search') and a precisely scoped resource ('the authenticated user's stored long-term memories'), plus the retrieval mechanism ('semantic similarity'). It does not, however, distinguish itself from nearby siblings such as rag_search or rag_similar, which an agent could easily confuse with this one.

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

Usage Guidelines3/5

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

The parenthetical '(preferences, facts, context)' implies the kind of content this tool is for, but there is no explicit when-to-use / when-not-to-use guidance and no alternative is named. An agent must infer on its own that rag_search covers documents rather than user memories.

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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