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330899935

hive-memory

by 330899935
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Python
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Recall memory

recall
Read-onlyIdempotent

Search confirmed memory by natural-language query to find what was previously decided, recorded, or agreed. Returns the closest stored entries, with empty results meaning no match.

Instructions

Search the human's confirmed memory by natural-language query and return the closest stored entries. Use this to find what was previously decided, recorded, or agreed. Do not use it at session start when you have no query yet - use get_context instead. Do not use it to review entries still awaiting approval - use manage with action list_pending. Annotations already declare this read-only, so this text covers what they cannot: results come from the confirmed store only, private vaults are omitted for agents not permitted to read them, and an empty list means no match rather than no memory.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of memories to return. Omit for the server default.
queryYesNatural-language search text, e.g. 'what did we decide about the pricing page'. Matched semantically, not by substring.
vaultsNoNarrow the search to these vault names. Omit to search every vault this agent may read. Naming a vault the agent may not read is not an error - it returns nothing from that vault.
compactNoReturn trimmed records (id, title, snippet) instead of full bodies. Saves tokens when you only need to know whether something exists.
query_vectorNoPrecomputed embedding. When supplied, recall ranks by vector similarity and ignores 'query' text matching.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.7

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already cover the read-only/destructive/idempotent profile, and the description deliberately adds what they cannot: results come only from the confirmed store, unreadable private vaults are silently omitted, and an empty list means no match rather than an empty memory. Those are exactly the non-obvious behaviors an agent would otherwise misread.

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

Conciseness4/5

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

The routing constraints and behavioral caveats are front-loaded and each sentence carries content. The one soft spot is the self-referential clause about what the annotations already declare, which is meta-commentary rather than tool guidance.

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?

No output schema exists, and the description compensates by explaining result scope (confirmed store only), permission-driven omission, and the meaning of an empty result set. Nothing an agent needs to call this correctly or interpret its response is missing.

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 limit, vaults, compact, query_vector and query are all fully documented in the schema. The description adds ranking framing ("closest stored entries") but says nothing about the parameters themselves, so the baseline of 3 applies.

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?

States a specific verb (search) and resource (the human's confirmed memory) plus the retrieval semantics (natural-language query, closest stored entries). It is immediately distinguishable from sibling get_context (session bootstrap) and manage (pending review), which the description names directly.

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

Usage Guidelines5/5

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

Gives explicit when-to-use (find what was decided, recorded, or agreed) and two explicit when-not-to-use cases, each paired with the correct alternative tool: get_context for session start with no query, manage action list_pending for unapproved entries. No inference required.

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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