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Relic-Studios

Deep Recall MCP Server

deeprecall_remember

Store memories that automatically build semantic relationships, detect contradictions, and consolidate into durable facts over time.

Instructions

Store a memory. Behind the scenes: embeds for semantic search, builds graph edges to related memories, detects contradictions, auto-resolves temporal changes, infers entity relationships, and periodically consolidates episode clusters into durable facts. All biology runs automatically — just store what matters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe memory to store
personNoWho this memory is about
kindNoMemory typefact
salienceNoImportance 0-1. Higher resists decay longer.
Behavior4/5

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

With no annotations, the description carries the full burden. It details internal processes like embedding, graph edges, contradiction detection, temporal resolution, and consolidation, which provides substantial transparency about side effects and automatic behaviors.

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

Conciseness3/5

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

The description is relatively concise but includes a long list of internal processes that may not be essential for an agent to know. It could be more streamlined by focusing on the core function.

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?

The description does not mention return values, side effects like success/failure responses, or prerequisites (e.g., authentication). Given no output schema, this omission is significant for an agent to understand the tool's full behavior.

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 baseline is 3. The description adds no additional meaning to the parameters beyond what the schema already provides, such as the enum for 'kind' or the range for 'salience'.

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 'Store a memory.' as the core action, with a specific verb and resource. It also distinguishes from the sibling tool 'deeprecall_search' which is for searching, not storing.

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 implies usage by stating 'just store what matters' and contrasts with the sibling 'deeprecall_search', providing implicit context. However, it does not explicitly state when not to use or provide alternative guidance.

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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