MCP Memento

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/annibale-x-mcp-memento

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What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

17 read · 5 write · 1 delete · 2 not yet classified

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Python package

Tools (25)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• adjust_memento_confidence

  Manually adjust confidence of a relationship. Use for: - Correcting confidence scores when you know a memory is valid/invalid - Setting custom confidence based on verification - Overriding automatic decay for specific cases Examples: - adjust_memento_confidence(relationship_id="rel-123", new_confidence=0.9, reason="Verified in production") - adjust_memento_confidence(relationship_id="rel-456", new_confidence=0.1, reason="Obsolete after library update") Confidence ranges: - 0.9-1.0: High confidence (recently validated) - 0.7-0.89: Good confidence (regularly used) - 0.5-0.69: Moderate confide…

• analyze_memento_graph

  Get comprehensive analytics and metrics for the memento graph

• apply_memento_confidence_decay

  Apply automatic confidence decay based on last access time. Use for: - System maintenance to keep knowledge base fresh - Applying intelligent decay rules - Monthly confidence adjustment routine Intelligent decay rules: - Critical memories (security, auth, api_key, password, critical, no_decay tags): NO DECAY - High importance memories: Reduced decay based on importance score - General knowledge: Standard 5% monthly decay (decay_factor=0.95) - Temporary context: Higher decay rate Decay formula: monthly_decay = confidence × decay_factor^(months_since_last_access) Minimum confidence: 0.1 (won…

• boost_memento_confidence

  Boost confidence when a memory is successfully used. Use for: - Reinforcing valid knowledge - Manual confidence increase for verified information - After successfully applying a solution - When verifying old information is still valid Usage patterns: - After successfully applying a solution → boost its confidence - When verifying old information is still valid → boost confidence - When multiple team members confirm a pattern → boost confidence Boost mechanics: - Base boost: +0.10 per access (capped at 1.0) - Additional boost for validation: +0.10 to +0.20 - Maximum confidence: 1.0 (cannot e…

• contextual_memento_search

  Search only within the context of a given memento (scoped search). Two-phase process: (1) Find related memories, (2) Search only within that set. Provides semantic scoping without embeddings. WHEN TO USE: - Searching within a specific problem context - Finding solutions in related knowledge - Scoped discovery HOW TO USE: - Specify memory_id (context root) - Provide query (search term) - Optional: max_depth (default: 2) RETURNS: - Matches found only within related memories - Context information - No leakage outside context

• create_memento_relationship

  Link two mementos with a typed relationship. Common types: SOLVES (solution→problem), CAUSES (cause→effect), ADDRESSES (fix→error), REQUIRES (dependent→dependency), RELATED_TO (general) EXAMPLES: - create_memento_relationship(from_memory_id="sol-1", to_memory_id="prob-1", relationship_type="SOLVES") - create_memento_relationship(from_memory_id="err-1", to_memory_id="fix-1", relationship_type="CAUSES", context="Config error caused timeout") Optional: strength (0-1), confidence (0-1), context (description)

• delete_memento

  Delete a memento and all its relationships

• find_memento_patterns

  Find patterns in mementos and relationships Analyzes the relationship graph to detect recurring structural patterns: - Most frequent relationship types - Common memory-type pairs connected by relationships Note: patterns require multiple occurrences of the same relationship type. With small datasets (< 10 relationships), lower the thresholds: min_pattern_size=1, min_support=0.0 → show all types, even singletons. When no patterns meet the thresholds the tool still reports all available relationship types with their counts so the result is always informative.

• find_path_between_mementos

  Find the shortest path between two mementos through relationships

• get_central_mementos

  Find mementos that connect different clusters (knowledge bridges)

• get_low_confidence_mementos

  Find memories with low confidence scores. Use for: - Identifying potentially obsolete knowledge - Periodic cleanup and verification - Quality assurance of the knowledge base - Finding memories that need review Features: - Filter by confidence threshold (default: < 0.3) - Shows relationships causing low confidence - Includes memory details and last access time - Sorted by confidence (lowest first) Returns: - List of low confidence relationships with associated memories - Memory details for both ends of each relationship - Confidence scores and last access times

• get_memento

  Retrieve a specific memento by ID. Use when you have a memory_id from search results or store_memento. Set include_relationships=true (default) to see connected memories. EXAMPLE: get_memento(memory_id="abc-123")

• get_memento_clusters

  Detect clusters of densely connected mementos

• get_memento_network

  Get the complete network structure of mementos and relationships

• get_memento_statistics

  Get statistics about the memento database

Signals

Stars

2

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Weekly downloads

54

Last commit

Apr 1, 2026 (7 months ago)

Last release

Apr 1, 2026 (7 months ago)

Last package publish

Apr 1, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.annibale-x/mcp-memento

Version

0.2.31

Auth

No auth

Packages

pypi mcp-memento

License

MIT

Repository

github.com/annibale-x/mcp-memento

First seen

Oct 24, 2026

Last checked

Oct 25, 2026

Source

Official MCP Registrygithub-graphql