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Store a fact in durable local memory with optional links to existing memories and metadata tags for later filtering. Set a time-to-live to automatically expire the fact.

Instructions

Store a fact in durable local memory. Optionally link it to existing memories (graph) and tag it with structured metadata like project/author/type/status/date (ColumnStore) for later filtering. Set ttl_seconds to make the fact expire after a delay (a durable TTL that survives restarts); omit it for a permanent memory. Returns the fact's stable id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
factYesThe fact to store in memory.
linksNoOptional typed links from this fact to existing memories.
metadataNoOptional structured metadata for later filtering (e.g. `{"project": "veles", "author": "julien", "status": "open"}`).
ttl_secondsNoOptional time-to-live in seconds. When set, the fact expires (and stops being recalled) after this many seconds — a durable TTL that survives a restart. Omit for a permanent memory. Falls back to the server's `VELESDB_MEMORY_DEFAULT_TTL` when unset.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesStable id assigned to the remembered fact.
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that storage is durable, TTL survives restarts, and returns a stable id. However, it does not mention behavior on duplicates or potential side effects.

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?

The description is three sentences, front-loaded with the core action, and every sentence adds value. It is efficient and well-structured.

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?

With 4 parameters, full schema coverage, and an output schema (not shown), the description covers purpose, all parameter behaviors, and the return value (stable id). It is complete for a store operation.

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 meaning: explains TTL_seconds' durable nature and default fallback, links as typed relations, and metadata for filtering. This adds value beyond 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 the primary action: 'Store a fact in durable local memory.' It distinguishes from siblings by mentioning optional linking and metadata tagging, which are unique features of this tool.

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 context on when to use optional parameters like links and metadata for later filtering, and explains TTL behavior. It does not explicitly mention when not to use this tool, but the context is sufficient.

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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