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alopez3006

snipara-mcp

by alopez3006

rlm_recall

Retrieve past decisions, learnings, preferences, and session carryover using semantic search. Filter by type, scope, category, and relevance thresholds for precise memory recall.

Instructions

Semantically recall durable Memory V2 records such as decisions, learnings, preferences, and session carryover. Not for source document retrieval; use rlm_context_query, rlm_load_document, or rlm_shared_context for specs, RFPs, diagrams, and raw docs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesMemory question such as a past decision, preference, or validated learning
typeNo
scopeNo
agent_idNoRequired when scope=agent; limits recall to one agent namespace
external_user_idNoIntegrator client keys only: stable end-user ID for scope=user recall. Snipara hashes and namespaces it per integrator client.
categoryNoFilter by category
limitNoMaximum memories to return
min_relevanceNoMinimum relevance score (0-1)
include_inactiveNoInclude inactive memories in the main result set
warning_thresholdNoMinimum relevance score for inactive-memory warnings
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It indicates a read-like operation (semantic recall) without explicitly stating side effects. It does not contradict any annotations (none exist) but could be more transparent about output behavior or limitations.

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 two sentences: the first clearly states the purpose, and the second provides usage boundaries. It is concise, front-loaded, and every word adds value.

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 the complexity (10 parameters, no output schema), the description is adequate but does not explain return format or pagination. It covers the essential usage distinction from siblings but lacks completeness on output details.

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 description coverage is 80% (high), so the baseline is 3. The description does not add parameter-specific details beyond what the schema provides, which is acceptable given the high coverage.

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 explicitly states the tool's purpose: 'Semantically recall durable Memory V2 records such as decisions, learnings, preferences, and session carryover.' It also distinguishes itself from siblings by listing alternative tools for different use cases.

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?

The description provides clear guidance on when to use this tool (for durable memory records) and when not to ('Not for source document retrieval'), naming specific alternatives like rlm_context_query, rlm_load_document, and rlm_shared_context.

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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