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memory_search

Search stored memories semantically to retrieve relevant context before decisions or at session start, with results ranked by relevance.

Instructions

Semantic search across all memories. Use this at the start of a session, before making decisions, or when you need context about a topic. Returns results ranked by relevance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
typeNo
limitNo
queryYesThe search query text
projectNo
min_scoreNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It does say the search is semantic and returns results 'ranked by relevance,' which is useful. But it does not explicitly state that the operation is read-only or side-effect free, nor does it mention behavior for no results or how relevance scoring works. Adequate but not rich.

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?

Three sentences with no wasted words: the core action is front-loaded, followed by usage timing, then a one-line statement about output order. Every sentence earns its place and the structure is ideal for quick parsing by an AI agent.

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?

For a 6-parameter tool with no annotations and no output schema, the description provides a strong high-level purpose and usage window but omits parameter semantics and output shape. An agent can issue a basic query, but may be unsure how tags, type, project, and min_score affect behavior or what fields the returned results contain.

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

Parameters2/5

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

Schema description coverage is only 17% (only query has a description), so the description must compensate. It does not explain tags, type, limit, project, or min_score. 'Ranked by relevance' hints at limit and min_score, and 'across all memories' suggests the default scope, but an agent cannot confidently use the filtering parameters based on the description alone.

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 opens with a specific verb-resource pair: 'Semantic search across all memories.' This clearly states what the tool does and distinguishes it from siblings like memory_list (which likely lists memories non-semantically). The added usage cues ('start of a session, before making decisions') reinforce its role as a context-retrieval tool.

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?

Explicit guidance is given for when to use the tool: 'at the start of a session, before making decisions, or when you need context about a topic.' However, it does not explicitly state when not to use it or name alternatives such as memory_list for exact listing, so it stops short of full exclusion logic.

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