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MCP Memento

by x-hannibal
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

memento_onboarding

Provides onboarding guidance for Memento's long-term memory system, including retrieval workflows, storage protocols, and best practices for cross-session knowledge management.

Instructions

Get comprehensive onboarding protocol for Memento including tool usage guidance, retrieval flow optimization, and best practices.

MEMENTO ONBOARDING PROTOCOL:

  1. INITIALIZATION: Run memento_onboarding() at session start

  2. RETRIEVAL FLOW:

    • Fact Check: Use search_mementos(tags=[...]) for simple identity/known facts

    • Complex Tasks: Use recall_mementos(query="...") for dev/architecture context

    • Fallback: If search fails, fallback to recall

  3. AUTOMATIC STORAGE: Store via store_memento on git commits, bug fixes, version releases

  4. ON-DEMAND TRIGGERS: Store instantly when user says "memento...", "remember...", etc.

  5. MEMORY SCHEMA: Required tags (project, tech, category). Importance: 0.8+ (critical), 0.5 (standard)

OPTIMIZED RETRIEVAL (Avoid 6+ tool calls):

  • Target: 1-3 tool calls for simple info

  • Maximum: 5 tool calls for complex tasks

  • Follow decision tree: Known tags → search_mementos, Conceptual → recall_mementos

CRITICAL DISTINCTION: Memento vs Session memory

  • Memento: Long-term, cross-session, global scope

  • Session Memory: Temporary, project-specific, session-only

USE memento_onboarding(topic="...") for specific guidance:

  • "protocol": Full onboarding protocol

  • "retrieval_flow": Optimized retrieval guide

  • "distinction": Memento vs Session memory

  • "examples": Practical examples

  • "best_practices": Usage guidelines

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoSpecific topic for onboarding guidanceonboarding

Implementation Reference

  • src/memento/tools/guide_tools.py:13-40 (handler)
    The primary handler for the 'memento_onboarding' tool, which processes the tool call and returns the onboarding content based on the specified topic.
    async def handle_memento_onboarding(
    context: Any, arguments: Dict[str, Any]
) -> CallToolResult:
    """
    Handle memento_onboarding tool call.

    Provides comprehensive onboarding protocol for Memento including
    tool usage guidance, retrieval flow optimization, and best practices.

    Args:
        context: Tool context with database and configuration
        arguments: Tool arguments including optional topic

    Returns:
        CallToolResult with onboarding content
    """
    topic = arguments.get("topic", "onboarding")

    onboarding_content = _generate_onboarding_content(topic)

    return CallToolResult(
        content=[
            TextContent(
                type="text",
                text=onboarding_content,
            )
        ]
    )
  • src/memento/tools/definitions.py:18-22 (registration)
    The tool registration where 'memento_onboarding' is defined, including its description, input schema, and purpose.
                name="memento_onboarding",
            description="""Get comprehensive onboarding protocol for Memento including tool usage guidance, retrieval flow optimization, and best practices.

MEMENTO ONBOARDING PROTOCOL:
1. INITIALIZATION: Run memento_onboarding() at session start

Tool Definition Quality

A4.3/5.0
Behavior3/5

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

No annotations are provided, so description must carry full burden. While it comprehensively details what content is returned (protocol sections, retrieval flows), it fails to explicitly disclose behavioral traits like read-only status, idempotency, or side effects that would normally be covered by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is quite verbose (effectively ~30 lines), but for an onboarding tool this length is functionally justified. It uses clear structural headers (MEMENTO ONBOARDING PROTOCOL, OPTIMIZED RETRIEVAL) that aid readability despite the bulk.

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

Completeness4/5

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

Given the single-parameter simplicity and lack of output schema, the description adequately compensates by exhaustively detailing what the tool returns. However, it could explicitly mention the return format (string vs structured object).

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?

Despite 100% schema coverage describing the 'topic' parameter, the description adds substantial semantic value by detailing exactly what content each enum value returns (e.g., 'protocol': Full onboarding protocol, 'retrieval_flow': Optimized retrieval guide).

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?

Description opens with specific verb ('Get') and resource ('comprehensive onboarding protocol for Memento'), clearly positioning this as a guidance/documentation tool that distinguishes itself from operational siblings like store_memento or search_mementos.

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 when-to-use directive ('Run memento_onboarding() at session start'), includes decision trees for sibling selection ('Known tags → search_mementos, Conceptual → recall_mementos'), and explicitly names alternative tools for specific tasks.

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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