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NeuralNexusNote

N3MemoryCore MCP — Lite (Ephemeral)

search_memory

Search stored memories using hybrid vector and keyword retrieval. Supports time range filtering and session-based boosting for relevant context.

Instructions

Hybrid (vector + BM25) search over stored memories. Call this at the start of every user turn. NOTE: Lite memories expire 7d after they were saved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (natural language or keywords).
limitNoMax results (default: config search_result_limit).
session_idNoOptional project/task grouping key. Rows whose stored session_id matches this value are boosted in ranking (b_session_match=1.0); non-matching rows are dampened (b_session_mismatch=0.6). Pass the same session_id used at save time to surface that project's memories above unrelated rows. Leave blank to use the server default (N3MC_SESSION_ID env var, or per-process UUIDv4).
sinceNo§4.3.1 — ISO 8601 date or datetime (e.g. '2026-05-01' or '2026-05-01T09:00:00Z'). Only entries saved ON OR AFTER this timestamp are returned. Date-only values are treated as 00:00:00 UTC.
untilNo§4.3.1 — ISO 8601 date or datetime. Only entries saved ON OR BEFORE this timestamp are returned. Date-only values are treated as 23:59:59 UTC.
Behavior3/5

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

Discloses the hybrid search method and the 7-day expiration for lite memories. No annotations exist, so the description carries the burden; it does not explicitly state read-only or non-destructive nature, but 'search' implies it.

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 with no wasted words. The purpose and usage guideline are front-loaded, making it efficient.

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

Completeness2/5

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

No output schema is provided, but the description does not explain what the tool returns (e.g., relevance scores, memory content). This leaves a significant information gap for the agent.

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 coverage is 100%, so the schema already describes all 5 parameters in detail. The description adds no additional parameter meaning beyond what is in the 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 clearly states it performs a 'Hybrid (vector + BM25) search over stored memories', which is specific and distinct from sibling tools like save_memory or delete_memory.

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?

Explicitly instructs to 'Call this at the start of every user turn', providing strong usage guidance. However, it does not mention when not to use it or explicitly compare to sibling tools.

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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