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memory_store

Store text content with metadata and automatic vector embedding for later semantic retrieval. Use this to save decisions, patterns, or knowledge.

Instructions

Store a new memory with content, metadata, and automatic vector embedding. Use this to save information, decisions, patterns, or knowledge for later semantic retrieval.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe text content to store as a memory
titleNoShort title for the memory
scopeNoMemory scope for isolation
namespaceNoNamespace within scope (e.g., project name, team name)
document_typeNoType of document (e.g., contract, policy, code, incident, decision)
sourceNoOrigin of the content (e.g., file path, URL, system name)
authorNoWho created this content
departmentNoDepartment (e.g., legal, engineering, hr, sales, finance)
tagsNoTags for categorization
access_levelNoAccess classification levelinternal
languageNoContent language (ISO 639-1 code)en
metadataNoDomain-specific metadata (e.g., {contract_type: 'NDA', parties: ['A','B']})
agent_idNoIdentifier of the writing agent for multi-agent attribution
expires_atNoFull ISO-8601 expiration timestamp, e.g. 2026-03-01T00:00:00Z (memory auto-excluded from search after this)
importance_scoreNoExplicit importance 0-1 (governance/criticality). When omitted it is derived from content; min_importance filters operate on this value.
on_conflictNoWrite policy when a near-match exists. "add" (default): insert as new, except an exact duplicate is skipped (NOOP) — identical to prior behaviour. "update": merge content into the existing match (append + re-embed + version bump). "supersede": retire (invalidate) the conflicting match and add this as the current one.add
volatilityNoOverride the auto-derived volatility class. Omit to auto-classify from content + document_type (volatile deploy/status facts warn sooner on recall).
verification_tierNoHow well this fact is verified: source_verified > tool_verified > asserted > unverified. Lowers groundedness for unverified claims. Omit ⇒ neutral.
verification_detailNoFree text: how/when/by-what the fact was verified (e.g. "checked live UAT DB 2026-06-18").
Behavior3/5

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

Annotations provide only openWorldHint=false, so the description carries the burden. It adds 'automatic vector embedding' but omits details on side effects, write policies (on_conflict), or permissions.

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?

A single, front-loaded sentence that efficiently conveys purpose and usage without unnecessary words.

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?

Despite 19 parameters and no output schema, the description is minimal. It omits crucial behavioral context like conflict resolution, expiration, volatility, and return value, leaving the agent to infer from the schema alone.

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% with detailed descriptions for each parameter. The tool description does not add extra meaning beyond summarizing 'content, metadata, and automatic vector embedding'. Baseline 3 is appropriate.

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 verb 'Store' and the resource 'a new memory', and distinguishes from sibling tools like memory_search and memory_delete by emphasizing saving for later retrieval. It also mentions automatic vector embedding, a key feature.

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

Usage Guidelines3/5

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

The description tells when to use the tool ('to save information, decisions, patterns, or knowledge for later semantic retrieval') but does not explicitly exclude scenarios or mention alternatives like core_memory_append or memory_update for handling conflicts.

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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