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N3MemoryCore MCP — Lite (Ephemeral)

N3MemoryCore MCP — Lite (Ephemeral)

by NeuralNexusNote
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

recall_thread

Retrieve a conversation thread around a specific turn_id to add context when a memory search result lacks sufficient surrounding information.

Instructions

§4.3.1 — Retrieve the memories sharing a turn_id plus N entries before and after in chronological order. Use this when a search_memory hit lacks enough context: pass the hit's turn_id here to surface the surrounding conversation thread. Returns {status:'not_found'} if turn_id is unknown.

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": {
      "turn_id": "<turn_id>"
    },
    "name": "recall_thread"
  }
}

Input Schema

NameRequiredDescriptionDefault
turn_idYesThe turn_id from a search_memory result.
beforeNoNumber of earlier entries to include (default 2).
afterNoNumber of later entries to include (default 2).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.1

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description discloses the retrieval behavior (chronological order), the error case (returns {status:'not_found'}), and the before/after parameters. It does not mention authentication or rate limits, but for a read-only retrieval tool, this is sufficient.

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 first sentence states the core action, the second gives usage context, and the third describes the return behavior. Information is front-loaded and efficiently presented.

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 no output schema, the description adequately describes the return format and error case. It covers the behavior of the parameters and provides a clear use case. No hidden behaviors remain unexplained.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds value by explaining the turn_id parameter's origin ('from a search_memory result') and contextualizing the before/after defaults within the narrative. This goes beyond the schema 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 clearly states the tool retrieves a thread of memories around a turn_id in chronological order, and explicitly distinguishes its use from search_memory by providing a specific use case ('when a search_memory hit lacks enough context').

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 when-to-use guidance ('Use this when a search_memory hit lacks enough context') and specifies the input (turn_id from a search_memory result). It does not explicitly list when not to use or alternative tools, but the sibling tools help the agent infer.

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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