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Chaerulcp

shared-agent-memory-mcp

by Chaerulcp

memory_search

Search shared long-term memory in Notion to retrieve saved preferences, conventions, and past decisions before starting tasks or making decisions.

Instructions

Search the shared long-term memory stored in Notion (used by Cline, OpenCode, Claude Code, GitHub Copilot, Hermes). Returns matching memories with id, title, content, agent, category, tags. USE THIS at the start of a task with relevant keywords, and before making decisions, to reuse saved preferences, conventions, and past decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNoExact tag name to filter by
agentNoOnly memories saved by this agent
limitNo
queryYesKeywords/phrase to match in title, content, and tags
categoryNo
Install Server

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden. It discloses that this searches shared cross-agent memory and specifies the returned fields. It does not mention pagination, result ordering, limit behavior, or explicitly confirm read-only semantics beyond the verb 'Search'.

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?

Two sentences, front-loaded with the action and resource, no filler. The parenthetical tool list adds useful scope context while remaining compact.

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 search tool with no output schema, it compensates by enumerating return fields and giving cross-agent context plus usage timing. It leaves minor gaps such as ordering and limit semantics, but these are partially covered by the schema, making it complete enough for invocation.

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?

The schema covers 60% of parameters with descriptions for query, tag, and agent, while limit and category rely on schema metadata. The description adds no parameter-level guidance beyond listing returned fields like category and tags, so it stays at baseline without compensating for the coverage gaps.

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 states a clear action ('Search') and specific resource ('shared long-term memory stored in Notion') and lists the returned fields. It does not explicitly contrast itself with siblings like memory_get or memory_recent, so it misses the top score for sibling differentiation.

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?

It gives an explicit directive: use at the start of a task with relevant keywords and before decisions, with concrete goals such as reusing saved preferences, conventions, and past decisions. It provides clear usage context but no 'when not to use' guidance or named alternatives.

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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