Memento MCP is a high-performance knowledge graph memory system for LLMs that enables semantic retrieval, contextual recall, and temporal awareness.
Key capabilities:
Entity Management: Create, retrieve, update, and delete entities with properties like name, type, observations, and metadata
Relation Management: Create, update, and delete relationships between entities with properties like strength and confidence
Graph Operations: Read the entire knowledge graph, search for nodes, and retrieve specific entities by name
Semantic Search: Search entities using vector embeddings and semantic similarity with configurable parameters
Temporal Features: Retrieve entity/relationship history, view graph state at specific timestamps, and apply time-decay to confidence values
Debugging Tools: Analyze embedding configuration and diagnose vector search operations
Enables GitHub Copilot to access the persistent knowledge graph memory system through the model context protocol.
Uses Neo4j as the storage backend for the knowledge graph, providing unified graph storage and vector search capabilities.
Leverages OpenAI's embedding models for semantic search capabilities, supporting multiple models including text-embedding-3-small/large.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@memento-mcp find all people who work at Anthropic and speak Spanish"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Scalable, high performance knowledge graph memory system with semantic retrieval, contextual recall, and temporal awareness. Provides any LLM client that supports the model context protocol (e.g., Claude Desktop, Cursor, Github Copilot) with resilient, adaptive, and persistent long-term ontological memory.
Entities are the primary nodes in the knowledge graph. Each entity has:
A unique name (identifier)
An entity type (e.g., "person", "organization", "event")
A list of observations
Vector embeddings (for semantic search)
Complete version history
Example:
{
"name": "John_Smith",
"entityType": "person",
"observations": ["Speaks fluent Spanish"]
}Relations define directed connections between entities with enhanced properties:
Strength indicators (0.0-1.0)
Confidence levels (0.0-1.0)
Rich metadata (source, timestamps, tags)
Temporal awareness with version history
Time-based confidence decay
Example:
{
"from": "John_Smith",
"to": "Anthropic",
"relationType": "works_at",
"strength": 0.9,
"confidence": 0.95,
"metadata": {
"source": "linkedin_profile",
"last_verified": "2025-03-21"
}
}Related MCP server: Graph Memory MCP
Memento MCP uses Neo4j as its storage backend, providing a unified solution for both graph storage and vector search capabilities.
Unified Storage: Consolidates both graph and vector storage into a single database
Native Graph Operations: Built specifically for graph traversal and queries
Integrated Vector Search: Vector similarity search for embeddings built directly into Neo4j
Scalability: Better performance with large knowledge graphs
Simplified Architecture: Clean design with a single database for all operations
Neo4j 5.13+ (required for vector search capabilities)
The easiest way to get started with Neo4j is to use Neo4j Desktop:
Download and install Neo4j Desktop from https://neo4j.com/download/
Create a new project
Add a new database
Set password to memento_password (or your preferred password)
Start the database
The Neo4j database will be available at:
Bolt URI: bolt://127.0.0.1:7687 (for driver connections)
HTTP: http://127.0.0.1:7474 (for Neo4j Browser UI)
Default credentials: username: neo4j, password: memento_password (or whatever you configured)
Alternatively, you can use Docker Compose to run Neo4j:
# Start Neo4j container
docker-compose up -d neo4j
# Stop Neo4j container
docker-compose stop neo4j
# Remove Neo4j container (preserves data)
docker-compose rm neo4jWhen using Docker, the Neo4j database will be available at:
Bolt URI: bolt://127.0.0.1:7687 (for driver connections)
HTTP: http://127.0.0.1:7474 (for Neo4j Browser UI)
Default credentials: username: neo4j, password: memento_password
Neo4j data persists across container restarts and even version upgrades due to the Docker volume configuration in the docker-compose.yml file:
volumes:
- ./neo4j-data:/data
- ./neo4j-logs:/logs
- ./neo4j-import:/importThese mappings ensure that:
/data directory (contains all database files) persists on your host at ./neo4j-data
/logs directory persists on your host at ./neo4j-logs
/import directory (for importing data files) persists at ./neo4j-import
You can modify these paths in your docker-compose.yml file to store data in different locations if needed.
You can change Neo4j editions and versions without losing data:
Update the Neo4j image version in docker-compose.yml
Restart the container with docker-compose down && docker-compose up -d neo4j
Reinitialize the schema with npm run neo4j:init
The data will persist through this process as long as the volume mappings remain the same.
If you need to completely reset your Neo4j database:
# Stop the container
docker-compose stop neo4j
# Remove the container
docker-compose rm -f neo4j
# Delete the data directory contents
rm -rf ./neo4j-data/*
# Restart the container
docker-compose up -d neo4j
# Reinitialize the schema
npm run neo4j:initTo back up your Neo4j data, you can simply copy the data directory:
# Make a backup of the Neo4j data
cp -r ./neo4j-data ./neo4j-data-backup-$(date +%Y%m%d)Memento MCP includes command-line utilities for managing Neo4j operations:
Test the connection to your Neo4j database:
# Test with default settings
npm run neo4j:test
# Test with custom settings
npm run neo4j:test -- --uri bolt://127.0.0.1:7687 --username myuser --password mypass --database neo4jFor normal operation, Neo4j schema initialization happens automatically when Memento MCP connects to the database. You don't need to run any manual commands for regular usage.
The following commands are only necessary for development, testing, or advanced customization scenarios:
# Initialize with default settings (only needed for development or troubleshooting)
npm run neo4j:init
# Initialize with custom vector dimensions
npm run neo4j:init -- --dimensions 768 --similarity euclidean
# Force recreation of all constraints and indexes
npm run neo4j:init -- --recreate
# Combine multiple options
npm run neo4j:init -- --vector-index custom_index --dimensions 384 --recreateFind semantically related entities based on meaning rather than just keywords:
Vector Embeddings: Entities are automatically encoded into high-dimensional vector space using OpenAI's embedding models
Cosine Similarity: Find related concepts even when they use different terminology
Configurable Thresholds: Set minimum similarity scores to control result relevance
Cross-Modal Search: Query with text to find relevant entities regardless of how they were described
Multi-Model Support: Compatible with multiple embedding models (OpenAI text-embedding-3-small/large)
Contextual Retrieval: Retrieve information based on semantic meaning rather than exact keyword matches
Optimized Defaults: Tuned parameters for balance between precision and recall (0.6 similarity threshold, hybrid search enabled)
Hybrid Search: Combines semantic and keyword search for more comprehensive results
Adaptive Search: System intelligently chooses between vector-only, keyword-only, or hybrid search based on query characteristics and available data
Performance Optimization: Prioritizes vector search for semantic understanding while maintaining fallback mechanisms for resilience
Query-Aware Processing: Adjusts search strategy based on query complexity and available entity embeddings
Track complete history of entities and relations with point-in-time graph retrieval:
Full Version History: Every change to an entity or relation is preserved with timestamps
Point-in-Time Queries: Retrieve the exact state of the knowledge graph at any moment in the past
Change Tracking: Automatically records createdAt, updatedAt, validFrom, and validTo timestamps
Temporal Consistency: Maintain a historically accurate view of how knowledge evolved
Non-Destructive Updates: Updates create new versions rather than overwriting existing data
Time-Based Filtering: Filter graph elements based on temporal criteria
History Exploration: Investigate how specific information changed over time
Relations automatically decay in confidence over time based on configurable half-life:
Time-Based Decay: Confidence in relations naturally decreases over time if not reinforced
Configurable Half-Life: Define how quickly information becomes less certain (default: 30 days)
Minimum Confidence Floors: Set thresholds to prevent over-decay of important information
Decay Metadata: Each relation includes detailed decay calculation information
Non-Destructive: Original confidence values are preserved alongside decayed values
Reinforcement Learning: Relations regain confidence when reinforced by new observations
Reference Time Flexibility: Calculate decay based on arbitrary reference times for historical analysis
Rich metadata support for both entities and relations with custom fields:
Source Tracking: Record where information originated (user input, analysis, external sources)
Confidence Levels: Assign confidence scores (0.0-1.0) to relations based on certainty
Relation Strength: Indicate importance or strength of relationships (0.0-1.0)
Temporal Metadata: Track when information was added, modified, or verified
Custom Tags: Add arbitrary tags for classification and filtering
Structured Data: Store complex structured data within metadata fields
Query Support: Search and filter based on metadata properties
Extensible Schema: Add custom fields as needed without modifying the core data model
The following tools are available to LLM client hosts through the Model Context Protocol:
create_entities
Create multiple new entities in the knowledge graph
Input: entities (array of objects)
Each object contains:
name (string): Entity identifier
entityType (string): Type classification
observations (string[]): Associated observations
add_observations
Add new observations to existing entities
Input: observations (array of objects)
Each object contains:
entityName (string): Target entity
contents (string[]): New observations to add
delete_entities
Remove entities and their relations
Input: entityNames (string[])
delete_observations
Remove specific observations from entities
Input: deletions (array of objects)
Each object contains:
entityName (string): Target entity
observations (string[]): Observations to remove
create_relations
Create multiple new relations between entities with enhanced properties
Input: relations (array of objects)
Each object contains:
from (string): Source entity name
to (string): Target entity name
relationType (string): Relationship type
strength (number, optional): Relation strength (0.0-1.0)
confidence (number, optional): Confidence level (0.0-1.0)
metadata (object, optional): Custom metadata fields
get_relation
Get a specific relation with its enhanced properties
Input:
from (string): Source entity name
to (string): Target entity name
relationType (string): Relationship type
update_relation
Update an existing relation with enhanced properties
Input: relation (object):
Contains:
from (string): Source entity name
to (string): Target entity name
relationType (string): Relationship type
strength (number, optional): Relation strength (0.0-1.0)
confidence (number, optional): Confidence level (0.0-1.0)
metadata (object, optional): Custom metadata fields
delete_relations
Remove specific relations from the graph
Input: relations (array of objects)
Each object contains:
from (string): Source entity name
to (string): Target entity name
relationType (string): Relationship type
read_graph
Read the entire knowledge graph
No input required
search_nodes
Search for nodes based on query
Input: query (string)
open_nodes
Retrieve specific nodes by name
Input: names (string[])
semantic_search
Search for entities semantically using vector embeddings and similarity
Input:
query (string): The text query to search for semantically
limit (number, optional): Maximum results to return (default: 10)
min_similarity (number, optional): Minimum similarity threshold (0.0-1.0, default: 0.6)
entity_types (string[], optional): Filter results by entity types
hybrid_search (boolean, optional): Combine keyword and semantic search (default: true)
semantic_weight (number, optional): Weight of semantic results in hybrid search (0.0-1.0, default: 0.6)
Features:
Intelligently selects optimal search method (vector, keyword, or hybrid) based on query context
Gracefully handles queries with no semantic matches through fallback mechanisms
Maintains high performance with automatic optimization decisions
get_entity_embedding
Get the vector embedding for a specific entity
Input:
entity_name (string): The name of the entity to get the embedding for
get_entity_history
Get complete version history of an entity
Input: entityName (string)
get_relation_history
Get complete version history of a relation
Input:
from (string): Source entity name
to (string): Target entity name
relationType (string): Relationship type
get_graph_at_time
Get the state of the graph at a specific timestamp
Input: timestamp (number): Unix timestamp (milliseconds since epoch)
get_decayed_graph
Get graph with time-decayed confidence values
Input: options (object, optional):
reference_time (number): Reference timestamp for decay calculation (milliseconds since epoch)
decay_factor (number): Optional decay factor override
Configure Memento MCP with these environment variables:
# Neo4j Connection Settings
NEO4J_URI=bolt://127.0.0.1:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=memento_password
NEO4J_DATABASE=neo4j
# Vector Search Configuration
NEO4J_VECTOR_INDEX=entity_embeddings
NEO4J_VECTOR_DIMENSIONS=1536
NEO4J_SIMILARITY_FUNCTION=cosine
# Embedding Service Configuration
MEMORY_STORAGE_TYPE=neo4j
OPENAI_API_KEY=your-openai-api-key
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
# Debug Settings
DEBUG=trueThe Neo4j CLI tools support the following options:
--uri <uri> Neo4j server URI (default: bolt://127.0.0.1:7687)
--username <username> Neo4j username (default: neo4j)
--password <password> Neo4j password (default: memento_password)
--database <n> Neo4j database name (default: neo4j)
--vector-index <n> Vector index name (default: entity_embeddings)
--dimensions <number> Vector dimensions (default: 1536)
--similarity <function> Similarity function (cosine|euclidean) (default: cosine)
--recreate Force recreation of constraints and indexes
--no-debug Disable detailed output (debug is ON by default)Available OpenAI embedding models:
text-embedding-3-small: Efficient, cost-effective (1536 dimensions)
text-embedding-3-large: Higher accuracy, more expensive (3072 dimensions)
text-embedding-ada-002: Legacy model (1536 dimensions)
To use semantic search, you'll need to configure OpenAI API credentials:
Obtain an API key from OpenAI
Configure your environment with:
# OpenAI API Key for embeddings
OPENAI_API_KEY=your-openai-api-key
# Default embedding model
OPENAI_EMBEDDING_MODEL=text-embedding-3-smallNote: For testing environments, the system will mock embedding generation if no API key is provided. However, using real embeddings is recommended for integration testing.
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"memento": {
"command": "npx",
"args": ["-y", "@gannonh/memento-mcp"],
"env": {
"MEMORY_STORAGE_TYPE": "neo4j",
"NEO4J_URI": "bolt://127.0.0.1:7687",
"NEO4J_USERNAME": "neo4j",
"NEO4J_PASSWORD": "memento_password",
"NEO4J_DATABASE": "neo4j",
"NEO4J_VECTOR_INDEX": "entity_embeddings",
"NEO4J_VECTOR_DIMENSIONS": "1536",
"NEO4J_SIMILARITY_FUNCTION": "cosine",
"OPENAI_API_KEY": "your-openai-api-key",
"OPENAI_EMBEDDING_MODEL": "text-embedding-3-small",
"DEBUG": "true"
}
}
}
}Alternatively, for local development, you can use:
{
"mcpServers": {
"memento": {
"command": "/path/to/node",
"args": ["/path/to/memento-mcp/dist/index.js"],
"env": {
"MEMORY_STORAGE_TYPE": "neo4j",
"NEO4J_URI": "bolt://127.0.0.1:7687",
"NEO4J_USERNAME": "neo4j",
"NEO4J_PASSWORD": "memento_password",
"NEO4J_DATABASE": "neo4j",
"NEO4J_VECTOR_INDEX": "entity_embeddings",
"NEO4J_VECTOR_DIMENSIONS": "1536",
"NEO4J_SIMILARITY_FUNCTION": "cosine",
"OPENAI_API_KEY": "your-openai-api-key",
"OPENAI_EMBEDDING_MODEL": "text-embedding-3-small",
"DEBUG": "true"
}
}
}
}Important: Always explicitly specify the embedding model in your Claude Desktop configuration to ensure consistent behavior.
For optimal integration with Claude, add these statements to your system prompt:
You have access to the Memento MCP knowledge graph memory system, which provides you with persistent memory capabilities.
Your memory tools are provided by Memento MCP, a sophisticated knowledge graph implementation.
When asked about past conversations or user information, always check the Memento MCP knowledge graph first.
You should use semantic_search to find relevant information in your memory when answering questions.Once configured, Claude can access the semantic search capabilities through natural language:
To create entities with semantic embeddings:
User: "Remember that Python is a high-level programming language known for its readability and JavaScript is primarily used for web development."To search semantically:
User: "What programming languages do you know about that are good for web development?"To retrieve specific information:
User: "Tell me everything you know about Python."The power of this approach is that users can interact naturally, while the LLM handles the complexity of selecting and using the appropriate memory tools.
Memento's adaptive search capabilities provide practical benefits:
Query Versatility: Users don't need to worry about how to phrase questions - the system adapts to different query types automatically
Failure Resilience: Even when semantic matches aren't available, the system can fall back to alternative methods without user intervention
Performance Efficiency: By intelligently selecting the optimal search method, the system balances performance and relevance for each query
Improved Context Retrieval: LLM conversations benefit from better context retrieval as the system can find relevant information across complex knowledge graphs
For example, when a user asks "What do you know about machine learning?", the system can retrieve conceptually related entities even if they don't explicitly mention "machine learning" - perhaps entities about neural networks, data science, or specific algorithms. But if semantic search yields insufficient results, the system automatically adjusts its approach to ensure useful information is still returned.
Memento MCP includes built-in diagnostic capabilities to help troubleshoot vector search issues:
Embedding Verification: The system checks if entities have valid embeddings and automatically generates them if missing
Vector Index Status: Verifies that the vector index exists and is in the ONLINE state
Fallback Search: If vector search fails, the system falls back to text-based search
Detailed Logging: Comprehensive logging of vector search operations for troubleshooting
Additional diagnostic tools become available when debug mode is enabled:
diagnose_vector_search: Information about the Neo4j vector index, embedding counts, and search functionality
force_generate_embedding: Forces the generation of an embedding for a specific entity
debug_embedding_config: Information about the current embedding service configuration
To completely reset your Neo4j database during development:
# Stop the container (if using Docker)
docker-compose stop neo4j
# Remove the container (if using Docker)
docker-compose rm -f neo4j
# Delete the data directory (if using Docker)
rm -rf ./neo4j-data/*
# For Neo4j Desktop, right-click your database and select "Drop database"
# Restart the database
# For Docker:
docker-compose up -d neo4j
# For Neo4j Desktop:
# Click the "Start" button for your database
# Reinitialize the schema
npm run neo4j:init# Clone the repository
git clone https://github.com/gannonh/memento-mcp.git
cd memento-mcp
# Install dependencies
npm install
# Build the project
npm run build
# Run tests
npm test
# Check test coverage
npm run test:coverageTo install memento-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @gannonh/memento-mcp --client claudeYou can run Memento MCP directly using npx without installing it globally:
npx -y @gannonh/memento-mcpThis method is recommended for use with Claude Desktop and other MCP-compatible clients.
For development or contributing to the project:
# Install locally
npm install @gannonh/memento-mcp
# Or clone the repository
git clone https://github.com/gannonh/memento-mcp.git
cd memento-mcp
npm installMIT
Add new observations to existing entities in your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| observations | Yes | ||
| strength | No | Default strength value (0.0 to 1.0) for all observations | |
| confidence | No | Default confidence level (0.0 to 1.0) for all observations | |
| metadata | No | Default metadata for all observations |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior but only states 'add new observations'. It does not specify what happens if the entity does not exist, whether observations are appended or overwritten, or any other side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no fluff, but it omits important context. It is concise but not optimally structured with key usage info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested object structure and multiple parameters, the description is too sparse. It lacks explanation of relationships between observations and entities, and no output schema is provided to compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75%, so the schema already explains most parameters. The description adds no further semantic value beyond the tool name, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb-resource pair ('Add new observations to existing entities') and clearly distinguishes from sibling tools like 'create_entities' or 'delete_observations'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No information is provided about when to use this tool versus alternatives (e.g., updating entities directly). No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create multiple new entities in your Memento MCP knowledge graph memory system
| Name | Required | Description | Default |
|---|---|---|---|
| entities | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It only states that the tool 'creates' entities, implying mutation, but provides no details on side effects, constraints, error conditions, or whether it is destructive. The description lacks sufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded with the core action. It contains no unnecessary words or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (batch creation with many optional fields), the absence of annotations and output schema, the description is minimal. It does not explain return values, error handling, batch limits, or how it relates to sibling tools like 'delete_entities' or 'read_graph'. The context is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides descriptions for all parameters, so the baseline is 3. The description does not add meaningful information beyond what the schema already states; it simply mentions 'multiple new entities' without detailing parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'create', the resource 'multiple new entities', and the context 'Memento MCP knowledge graph memory system'. It effectively distinguishes from sibling tools like 'create_relations' and 'add_observations'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., when to use create_entities vs add_observations). There is no mention of prerequisites, exclusions, or use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create multiple new relations between entities in your Memento MCP knowledge graph memory. Relations should be in active voice
| Name | Required | Description | Default |
|---|---|---|---|
| relations | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as idempotency, side effects, permissions, or error conditions. The only extra information is 'active voice' style.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (2 sentences) but lacks substance. It is concise but not effectively structured for quick comprehension of tool usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complex nested schema (multiple relation properties), no output schema, and many sibling tools, the description is too sparse. It does not address error handling, success behavior, or relationships to other tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is reported as 0%, meaning the input schema's own descriptions are not counted. The tool description adds minimal parameter insight beyond the schema: 'Relations should be in active voice' does not clarify parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action 'create multiple new relations' and the target resource 'entities in your Memento MCP knowledge graph memory'. It distinguishes from sibling tools like delete_relations and update_relation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., update_relation). The 'active voice' note is a stylistic hint but not a usage guideline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete multiple entities and their associated relations from your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| entityNames | Yes | An array of entity names to delete |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It notes cascading deletion of relations, a key trait, but does not mention prerequisites, reversibility, or limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence description is concise (12 words), front-loaded with the verb and object, and includes all essential information without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple delete tool with one parameter and no output schema, the description covers the main action and scope. It could mention permanence but is otherwise adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the parameter description is clear. The tool description adds no extra semantic meaning beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes multiple entities and their associated relations, distinguishing it from sibling tools like delete_observations or delete_relations that handle different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (when deleting entities and their relations), but lacks explicit when-not or alternative tool mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete specific observations from entities in your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| deletions | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states 'delete', implying mutation, but lacks details on side effects, irreversibility, permissions, or what happens if observations don't exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the verb 'Delete', no extraneous information. Every word contributes to understanding the tool's core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is too brief. It does not explain the deletions parameter format, behavior on missing entities or observations, or any return value. Leaves too many gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% (per signal), so description should compensate. It does not mention the nested structure with entityName and observations. The schema provides definitions, but the description adds no value beyond it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action (delete), resource (observations), and context (Memento MCP knowledge graph memory). Distinguishes from sibling tools like delete_entities and delete_relations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Does not mention that delete_entities or delete_relations are for other resource types, or any prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete multiple relations from your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| relations | Yes | An array of relations to delete |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description does not disclose any side effects, error states, or constraints beyond the action of deletion. For a delete operation, more detail on idempotency or cascade effects would be expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (11 words), but it sacrifices necessary context. It is minimally adequate but not an example of efficient depth.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description lacks details on return values, error handling, or behavioral context. It is incomplete for a tool with a single, complex parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes all parameters. The description adds no additional meaning beyond the schema, earning a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes multiple relations, with a specific verb and resource. It distinguishes from siblings like create_relations and get_relation, though the scope is implied rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives (e.g., no mention of deleting single relations vs batch, or when to prefer this over update_relation). The context is missing entirely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get your Memento MCP knowledge graph memory with confidence values decayed based on time
| Name | Required | Description | Default |
|---|---|---|---|
| reference_time | No | Optional reference timestamp (in milliseconds since epoch) for decay calculation | |
| decay_factor | No | Optional decay factor override (normally calculated from half-life) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose whether the tool is read-only, destructive, or requires special permissions. It does not explain the decay mechanism or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words, but slightly vague. Could be more informative while remaining concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Missing return value description (no output schema) and behavioral details. Adequate for a simple retrieval, but incomplete given no annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description adds context by linking parameters (reference_time, decay_factor) to the decay behavior, but does not elaborate further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a knowledge graph with decayed confidence values, distinguishing it from siblings like get_graph_at_time and read_graph.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., get_graph_at_time, semantic_search), nor any when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the vector embedding for a specific entity from your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| entity_name | Yes | The name of the entity to get the embedding for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must convey behavioral traits. It only states the action without disclosing side effects, read-only nature, performance characteristics, or any constraints. The agent cannot deduce that this is a safe read operation without additional context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded. However, it is somewhat terse and could benefit from slight expansion without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description is minimally complete. However, it lacks mention of the return type (vector embedding) and does not clarify that it is a read-only operation, which would be helpful for agents.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already describes the parameter 'entity_name'. The description adds only the phrase 'from your Memento MCP knowledge graph memory', which provides context but no additional semantic detail about the parameter itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves a vector embedding for a specific entity, using a specific verb ('Get') and resource. It mentions the knowledge graph context, but does not explicitly differentiate from siblings like 'get_entity_history' or 'semantic_search', which are distinct but also involve entities/embeddings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it does not explain how this differs from 'semantic_search' which also uses embeddings, or when to prefer 'get_entity_embedding' over 'get_entity_history'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the version history of an entity from your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| entityName | Yes | The name of the entity to retrieve history for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry the burden of behavioral disclosure. It only states retrieval of history but does not mention whether it is read-only, any rate limits, or side effects like data mutation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no fluff, efficiently conveying the purpose. However, it could include more detail without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one required parameter, no output schema, no nested objects), the description is adequate but lacks mention of what the version history format includes or any temporal context, which is relevant given siblings like get_graph_at_time.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the only parameter 'entityName'. The description adds no additional meaning beyond what the schema already provides, earning a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (get), the resource (version history of an entity), and the context (from Memento MCP knowledge graph memory). It distinguishes itself from siblings like get_relation_history by specifying 'entity'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as get_entity_embedding or get_graph_at_time. There is no mention of prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get your Memento MCP knowledge graph memory as it existed at a specific point in time
| Name | Required | Description | Default |
|---|---|---|---|
| timestamp | Yes | The timestamp (in milliseconds since epoch) to query the graph at |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states a read operation without mentioning performance implications, return format, or any potential side effects. Essential context is missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence with no wasted words. It conveys the core functionality efficiently and is easily scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is minimal. It does not explain the output format, limitations on time range or precision, or how this tool relates to other time-based tools. An agent would need additional information to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the timestamp parameter with high coverage (100%), including its unit (milliseconds since epoch). The description adds no additional semantic value beyond what the schema provides, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'Memento MCP knowledge graph memory' with a specific temporal scope 'as it existed at a specific point in time'. This effectively differentiates it from siblings like read_graph (current state) and get_decayed_graph (decayed state).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for historical queries but provides no explicit guidance on when to use this tool versus alternatives like get_entity_history or get_decayed_graph. No exclusion criteria or alternative names are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a specific relation with its enhanced properties from your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| from | Yes | The name of the entity where the relation starts | |
| to | Yes | The name of the entity where the relation ends | |
| relationType | Yes | The type of the relation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only says 'enhanced properties' without explaining behavior (e.g., side effects, permissions). It does not reveal what enhanced properties are.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One concise sentence, front-loaded with purpose, though 'from your Memento MCP knowledge graph memory' is slightly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and no annotations; the description lacks details about return format, pagination, or error conditions, leaving the agent under-informed for a get operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and each parameter is described. The description adds no extra meaning beyond the schema, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'get' and the resource 'a specific relation', including 'enhanced properties', which distinguishes it from sibling tools like create_relations or delete_relations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like get_relation_history or read_graph. The description does not mention prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the version history of a relation from your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| from | Yes | The name of the entity where the relation starts | |
| to | Yes | The name of the entity where the relation ends | |
| relationType | Yes | The type of the relation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden for behavioral disclosure. However, it only states 'get version history', omitting traits like read-only nature, authorization requirements, or whether history includes changes to properties or just the relation's existence. Minimal behavioral context provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no redundancy or filler. Front-loaded with the core action and resource. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description should explain what is returned (e.g., list of versions, timestamps, field changes). It does not, leaving the agent uncertain about the response format. Also lacks details on ordering, pagination, or limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%; each parameter (from, to, relationType) has a clear description. The description does not add meaning beyond the schema, meeting the baseline expectation. No additional parameter details like format or constraints are offered.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Get the version history of a relation'. It specifies the verb (get), resource (relation), and scope (version history), distinguishing it from sibling tools like 'get_relation' (current state) and 'get_entity_history' (entity version history).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., 'get_relation' for current state, 'get_graph_at_time' for historical snapshots). No prerequisites or context provided, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Open specific nodes in your Memento MCP knowledge graph memory by their names
| Name | Required | Description | Default |
|---|---|---|---|
| names | Yes | An array of entity names to retrieve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. The description only states it 'opens' nodes, but does not clarify whether the operation is read-only, what side effects exist, or what happens if a node is not found. This is insufficient for safe tool invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no filler. It is front-loaded with the action. However, it is borderline too terse, missing important details that could be included without significant length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and no annotations, yet the description does not explain what the tool returns (e.g., full node data, status messages). Given the complexity of the knowledge graph context and many sibling tools, this lack of completeness hinders effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema documents the parameter 'names' as an array of strings. The description adds minimal extra meaning ('by their names') that aligns with the schema. No additional details like name format, case sensitivity, or behavior for missing names are provided, keeping it at the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Open') and resource ('nodes'), and adds the qualifier 'by their names', which clarifies the tool's action. However, it does not explicitly differentiate this from other retrieval tools like 'read_graph' or 'search_nodes', leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., search_nodes, read_graph). There are no exclusions or context hints, forcing the agent to infer usage from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the entire Memento MCP knowledge graph memory system
| Name | Required | Description | Default |
|---|---|---|---|
| random_string | No | Dummy parameter for no-parameter tools |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes a read operation (non-destructive) but omits details about size constraints, timeouts, or permissions. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no redundancy. All words are necessary and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, no annotations. Reading the entire graph could be heavy; description lacks warnings or suggestions for partial reads via sibling tools. Incomplete given tool complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one dummy parameter explained. Description adds no new meaning beyond schema; baseline 3 applies as schema suffices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the verb 'Read' and the resource 'entire Memento MCP knowledge graph memory system'. It distinguishes from siblings like 'get_graph_at_time' or 'get_decayed_graph' which offer subsets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for retrieving the full graph, but no explicit guidance on when to use versus alternatives like 'search_nodes' or 'semantic_search'. No when-not-to-use or prerequisite info.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for nodes in your Memento MCP knowledge graph memory based on a query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query to match against entity names, types, and observation content |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'based on a query' but omits return format, pagination, or read-only nature, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence, front-loaded with the action and resource, containing no superfluous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is adequate but lacks information on what the search returns or how it differs from similar tools like semantic_search.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of parameters, and the description adds value by specifying that the query matches 'entity names, types, and observation content', which clarifies the parameter's usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Search for nodes' and the resource 'Memento MCP knowledge graph memory', but does not differentiate from sibling tools like semantic_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs alternatives (e.g., semantic_search). The description only states what it does, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for entities semantically using vector embeddings and similarity in your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The text query to search for semantically | |
| limit | No | Maximum number of results to return (default: 10) | |
| min_similarity | No | Minimum similarity threshold from 0.0 to 1.0 (default: 0.6) | |
| entity_types | No | Filter results by entity types | |
| hybrid_search | No | Whether to combine keyword and semantic search (default: true) | |
| semantic_weight | No | Weight of semantic results in hybrid search from 0.0 to 1.0 (default: 0.6) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It does not mention return format, result interpretation, performance implications, or safety traits. Only states the action without side-effect details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that efficiently communicates the tool's purpose with no redundancy. All words contribute to clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 6 parameters, no output schema, and no annotations, the description provides only a high-level overview. Missing details on return values, pagination, and specific behaviors for parameters like hybrid_search or entity_types.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already documented in the schema. The description adds no additional meaning beyond the schema, meeting the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for entities semantically using vector embeddings and similarity, specifying the resource (Memento MCP knowledge graph memory) and methodology. It distinguishes from sibling tools like search_nodes which likely use keyword search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., search_nodes, open_nodes). Does not mention prerequisites or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an existing relation with enhanced properties in your Memento MCP knowledge graph memory
| Name | Required | Description | Default |
|---|---|---|---|
| relation | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It indicates mutation but does not mention idempotency, error cases (e.g., relation not found), or side effects. The description is too brief to provide transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It is front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with a complex nested parameter structure and no output schema, the description omits return values, error handling, and behavioral specifics. It does not fully enable an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description text does not describe any parameters; all parameter meaning comes from the schema itself. Since schema description coverage is 0% from the description's perspective, it fails to add value beyond the structured schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Update', the resource 'existing relation', and the context 'in your Memento MCP knowledge graph memory'. It distinguishes from siblings like create_relations (create vs update) and delete_relations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives (e.g., when to update vs create, or prerequisites like relation existence). Usage is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: CRUD operations for entities, relations, and observations are separated, and specialized tools for history, embeddings, and time-specific queries do not overlap. No ambiguity between tool functions.
All tool names follow a consistent verb_noun pattern (e.g., create_entities, delete_observations, get_entity_history). The naming is uniform and predictable, aiding agent selection.
With 17 tools, the count is slightly above the ideal 3-15 range but still well-scoped for a knowledge graph system. Each tool addresses a specific need, though a few could potentially be consolidated.
The tool surface covers most CRUD operations but lacks an update_entity tool and a dedicated get_entity (though open_nodes and read_graph partially fill this). Missing update for observations. These gaps may cause some workflow interruptions.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Company brain for AI agents — temporal knowledge graph search, exploration, and durable memory.
Persistent AI memory with semantic search, conflict detection, and ticketing.
Memory system for AI agents with semantic search. Store and recall memories with ease.
Shared long-term memory for AI agents: save and recall context as a searchable knowledge graph.
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