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alopez3006

snipara-mcp

by alopez3006

rlm_remember

Save structured memory records (fact, decision, learning, preference, todo, context) for later semantic retrieval. Set ownership scope to agent, project, team, or user.

Instructions

Store a durable Memory V2 record for later semantic recall. Direct writes support fact, decision, learning, preference, todo, and context. Use the narrowest owner scope: agent for one agent role, project for one client/project/RFP, team for reviewed shared standards, and user for one person's preferences. Do not store source truth here; use rlm_context_query, rlm_load_document, or rlm_shared_context for specs, RFPs, diagrams, and raw docs. Use rlm_end_of_task_commit for workflow capture.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoThe memory text to store
contentNoDEPRECATED: Use 'text' instead. The memory content to store.
typeNofact
scopeNoMemory owner boundary. scope=agent requires agent_id; scope=user is personal to the authenticated user or integrator external_user_id; scope=team requires the current project to belong to a team.project
agent_idNoRequired when scope=agent; identifies the agent-owned memory namespace
external_user_idNoIntegrator client keys only: stable end-user ID for scope=user memory. Snipara hashes and namespaces it per integrator client.
categoryNoOptional category for grouping
ttl_daysNoDays until expiration (null = permanent)
related_toNoIDs of related memories
document_refsNoReferenced document paths
sourceNoOptional source label for the memory write
Behavior3/5

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

No annotations provided, so description carries full burden. It describes the tool as a direct write operation for durable storage, implying mutation. However, it lacks details on side effects, idempotency, error handling, or authentication requirements. While it communicates core behavior, it could be more transparent.

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 description is concise, front-loaded with the primary action, and organized in a single paragraph. Every sentence adds value, though it could be slightly more structured with bullet points for scopes. Still, it's efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 11 parameters, zero required, and no output schema, the description covers core usage and alternatives adequately. However, it does not describe the return value or error conditions, which would help completion. The guidance on scope and type is good, but lacking output details lowers completeness.

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 high (91%), so baseline is 3. The description adds value by explaining the semantic meaning of scopes (e.g., 'agent for one agent role') and lists types, which aids correct usage beyond the schema's basic 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 clearly states the tool's purpose: 'Store a durable Memory V2 record for later semantic recall.' It lists supported types (fact, decision, etc.) and distinguishes from siblings by explicitly naming alternative tools for different use cases (rlm_context_query, rlm_load_document, rlm_end_of_task_commit).

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?

Provides explicit guidance on when to use vs. when not to: 'Use the narrowest owner scope' with definitions for each scope, and explicitly warns 'Do not store source truth here' with alternative tools listed. Also directs workflow capture to rlm_end_of_task_commit. This clearly helps an agent decide.

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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