Skip to main content
Glama

Store Memory

montycat_remember

Store a fact or record in persistent memory; it is automatically embedded and indexed for quick semantic or key-based recall later.

Instructions

Store a fact or record in memory; it is embedded and indexed automatically.

Later recall it by meaning with montycat_semantic_search, or by key with montycat_recall. Returns the generated key in payload.

Every record is auto-stamped with an indexed _created_at (UTC ISO-8601) unless the value already carries one — this powers time-range recall (since/until on montycat_semantic_search). Top-level fields are indexed, so they can be used as filters in hybrid search (e.g. store {"project": "x", ...}, later filter on it).

Args: value: The record to store (a JSON object). scope: Owner/user id to store under (that owner's private memory, keyspace mem_). Use "shared" for the common keyspace. keyspace: Explicit keyspace override (advanced; bypasses scope). custom_key: Optional stable key to store under (for later exact recall/update). timestamp: Index a _created_at for time-range recall. Defaults to MONTYCAT_AUTO_TIMESTAMP (on). Pass False to skip the server-side timestamp parse when this memory will never be recalled by time. wait_for_index: For persistent keyspaces, wait until secondary indexes have caught up before returning. Defaults to the engine setting; use True when an immediate filtered/semantic recall must see this write. vector: Optional precomputed embedding for this record. It must match the keyspace's enrolled embedding profile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNo
valueYes
vectorNo
keyspaceNo
timestampNo
custom_keyNo
wait_for_indexNo
Install Server

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the sparse annotations by disclosing automatic embedding and indexing, auto-stamping of _created_at, indexing of top-level fields for filters, wait_for_index catch-up semantics, and the return behavior. It adds substantial behavioral context that an agent needs to understand side effects and timing, with no contradiction of annotations.

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 appropriately sized for the tool's complexity. It front-loads the primary purpose, then systematically covers return value, timestamp behavior, filtering implications, and a clear Args block. Every sentence adds value; no filler or redundancy.

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?

Given the tool's complexity, 7 parameters, no output schema, and minimal annotations, the description covers everything needed: purpose, key return field, timestamp semantics, filtering via indexed top-level fields, per-parameter guidance, and advanced keyspace behavior. It is complete enough for an agent to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden. It provides detailed, meaningful explanations for all 7 parameters, including defaults, advanced usage, requirements (vector must match profile), and behavioral implications (wait_for_index). It fully compensates for the missing schema descriptions.

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 opens with a specific verb and resource: 'Store a fact or record in memory'. It immediately clarifies the core behavior (embedding and automatic indexing) and distinguishes itself from recall tools by naming them as follow-ups (montycat_semantic_search, montycat_recall). It is clearly differentiated from the sibling 'remember_bulk' by implication of a single record.

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 gives context about when storage matters ('Later recall it by meaning...'), but does not explicitly state when to choose this tool over alternatives like montycat_remember_bulk or montycat_update. No exclusions or explicit routing guidance are provided, so usage is mostly implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MontyGovernance/montycat-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server