Skip to main content
Glama
flrngel

Fuzzy Memory MCP Server

by flrngel
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Knowledge Graph Memory Server (from official site enhanced with Fuzzy Search)

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats. This version has been enhanced with fuse.js to provide fuzzy, semantic searching capabilities.

Instruction prompt. (Add this to your CLAUDE.md or any other related)

Here is an instruction guide for each tool, focusing on best practices for using the knowledge graph effectively.

### A Guide to Using Knowledge Graph Memory

This guide outlines best practices for interacting with your knowledge graph memory. Following these principles will help you build a clean, accurate, and useful memory over time. The core idea is to first **search** for what you know, then **act** to add, update, or remove information.

---

#### **`search_nodes`**

This is your primary tool for discovery. It performs a fuzzy search across all entity names, types, and observations to find relevant information.

*   **Best Practice:** Always search before you create. To avoid creating duplicate entities (e.g., "Jane_Doe" when "Jane_Doe_Dev" already exists), start with a broad search to see what the graph already knows.
*   **Invocation Tip:** Use conceptual queries. You don't need an exact name. A query like "project manager who likes dogs" will effectively search observations across all entities to find the best match. Review the returned `score` to understand the confidence of the match.

---

#### **`create_entities`**

Use this to establish a new person, place, organization, or concept as a node in your graph.

*   **Storage Tip:** Choose a consistent and unique `name` for each entity (e.g., `FirstName_LastName`, `Project_Name`). This name is the permanent identifier.
*   **Invocation Tip:** Create entities with a few core `observations` from the start. An entity is more useful when it's created with initial facts, such as "is a software engineer" or "founded in 2021".

---

#### **`add_observations`**

Use this to add new facts or attributes to an entity that already exists.

*   **Storage Tip:** Keep observations atomic. Each observation should represent a single, discrete fact (e.g., use "Loves hiking" and "Lives in Colorado" as two separate observations, not one). This makes information easier to manage and remove later.
*   **Invocation Tip:** This tool is for enriching existing entities. It will not add duplicate observations, so you can safely call it with a list of facts without worrying about creating redundant entries.

---

#### **`create_relations`**

This tool connects two existing entities with a directed, active-voice relationship (e.g., `Jane_Doe` -> `reports_to` -> `John_Smith`).

*   **Storage Tip:** Ensure both the `from` and `to` entities already exist before creating a relation between them. A relation is meaningless without its nodes.
*   **Invocation Tip:** Use a consistent vocabulary for `relationType` (e.g., always use `works_at`, not a mix of `works_at` and `employed_by`). This makes the graph structure predictable and easier to query.

---

#### **`open_nodes`**

Use this to retrieve one or more entities by their exact name, along with any relations that exist between them.

*   **Best Practice:** Use this when you know the exact name of an entity and want to see its details and local connections. It's more precise than `search_nodes` for targeted lookups.
*   **Invocation Tip:** Before updating or deleting, use `open_nodes` to inspect the entity and its relationships. This helps confirm you are targeting the correct information.

---

#### **`delete_observations`**

This tool removes specific facts from an entity.

*   **Best Practice:** This is the standard way to update an entity when a fact is no longer true (e.g., removing "is learning Spanish" after proficiency is achieved).
*   **Invocation Tip:** You must provide the *exact* text of the observation to be deleted. Use `open_nodes` first to retrieve the exact phrasing if you are unsure.

---

#### **`delete_relations`**

This tool removes a specific connection between two entities, leaving the entities themselves intact.

*   **Best Practice:** Use this to update the graph when a relationship changes. For example, if a person moves to a new team, you would delete their old `reports_to` relation.
*   **Invocation Tip:** To be successful, the call must exactly match the `from` entity, `to` entity, and `relationType` of the stored relation.

---

#### **`delete_entities`**

This is a destructive action that permanently removes an entity and all relations connected to it.

*   **Best Practice:** Be certain before using this tool. Deleting an entity causes a cascading delete of all its connections. If you only want to remove an incorrect fact, use `delete_observations` instead.
*   **Invocation Tip:** The tool will not fail if the entity doesn't exist, so you don't need to check for its existence before calling.

---

#### **`read_graph`**

This tool retrieves the entire knowledge graph—every entity and every relation.

*   **Best Practice:** Use this tool sparingly, as it can return a very large amount of data. It is best suited for offline analysis, debugging, or getting a complete overview of your memory.
*   **Invocation Tip:** For nearly all interactive tasks, prefer the more focused `search_nodes` or `open_nodes` tools for better performance and relevance.

Related MCP server: Knowledge Graph Memory Server

Core Concepts

Entities

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

Example:

{
  "name": "John_Smith",
  "entityType": "person",
  "observations": ["Speaks fluent Spanish"]
}

Relations

Relations define directed connections between entities. They are always stored in active voice and describe how entities interact or relate to each other.

Example:

{
  "from": "John_Smith",
  "to": "Anthropic",
  "relationType": "works_at"
}

Observations

Observations are discrete pieces of information about an entity. They are:

  • Stored as strings

  • Attached to specific entities

  • Can be added or removed independently

  • Should be atomic (one fact per observation)

Example:

{
  "entityName": "John_Smith",
  "observations": [
    "Speaks fluent Spanish",
    "Graduated in 2019",
    "Prefers morning meetings"
  ]
}

API

Tools

  • 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

    • Ignores entities with existing names

  • create_relations

    • Create multiple new relations between entities

    • Input: relations (array of objects)

      • Each object contains:

        • from (string): Source entity name

        • to (string): Target entity name

        • relationType (string): Relationship type in active voice

    • Skips duplicate relations

  • 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

    • Returns added observations per entity

    • Fails if entity doesn't exist

  • delete_entities

    • Remove entities and their relations

    • Input: entityNames (string[])

    • Cascading deletion of associated relations

    • Silent operation if entity doesn't exist

  • delete_observations

    • Remove specific observations from entities

    • Input: deletions (array of objects)

      • Each object contains:

        • entityName (string): Target entity

        • observations (string[]): Observations to remove

    • Silent operation if observation doesn't exist

  • 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

    • Silent operation if relation doesn't exist

  • read_graph

    • Read the entire knowledge graph

    • No input required

    • Returns complete graph structure with all entities and relations

  • search_nodes

    • Performs a fuzzy semantic search for nodes in the knowledge graph using Fuse.js

    • Input: query (string)

    • Searches across:

      • Entity names

      • Entity types

      • Observation content

    • Returns an array of search results, each containing:

      • entity: The matched entity object

      • score: Confidence score from 0.0 to 1.0 (higher is better)

    • Uses fuzzy matching with:

      • Threshold: 0.6 (0.0 = perfect match, 1.0 = matches anything)

      • Minimum match character length: 2

      • Location-independent matching

  • open_nodes

    • Retrieve specific nodes by name

    • Input: names (string[])

    • Returns:

      • Requested entities

      • Relations between requested entities

    • Silently skips non-existent nodes

Usage with Claude Desktop

Setup

Add this to your claude_desktop_config.json:

NPX

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "github:flrngel/fuzzy-memory-mcp#main"
      ]
    }
  }
}

NPX with custom setting

The server can be configured using the following environment variables:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "github:flrngel/fuzzy-memory-mcp#main"
      ],
      "env": {
        "MEMORY_FILE_PATH": "/path/to/custom/memory.json"
      }
    }
  }
}

  • MEMORY_FILE_PATH: Path to the memory storage JSON file (default: memory.json in the server directory)

VS Code Installation Instructions

For quick installation, use one of the one-click installation buttons below:

Install with NPX in VS Code Install with NPX in VS Code Insiders

Install with Docker in VS Code Install with Docker in VS Code Insiders

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open Settings (JSON).

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

Note that the mcp key is not needed in the .vscode/mcp.json file.

NPX

{
  "mcp": {
    "servers": {
      "memory": {
        "command": "npx",
        "args": [
          "-y",
          "github:flrngel/fuzzy-memory-mcp#main"
        ]
      }
    }
  }
}

System Prompt

The prompt for utilizing memory depends on the use case. Changing the prompt will help the model determine the frequency and types of memories created.

Here is an example prompt for chat personalization. You could use this prompt in the "Custom Instructions" field of a Claude.ai Project. 

Follow these steps for each interaction:

1. User Identification:
   - You should assume that you are interacting with default_user
   - If you have not identified default_user, proactively try to do so.

2. Memory Retrieval:
   - Always begin your chat by saying only "Remembering..." and retrieve all relevant information from your knowledge graph
   - Always refer to your knowledge graph as your "memory"

3. Memory
   - While conversing with the user, be attentive to any new information that falls into these categories:
     a) Basic Identity (age, gender, location, job title, education level, etc.)
     b) Behaviors (interests, habits, etc.)
     c) Preferences (communication style, preferred language, etc.)
     d) Goals (goals, targets, aspirations, etc.)
     e) Relationships (personal and professional relationships up to 3 degrees of separation)

4. Memory Update:
   - If any new information was gathered during the interaction, update your memory as follows:
     a) Create entities for recurring organizations, people, and significant events
     b) Connect them to the current entities using relations
     b) Store facts about them as observations

Building and Development

Prerequisites

  • Node.js and npm

  • Docker (for building the Docker image)

Local Development

If you've cloned this repository and want to run the server locally for development:

  1. Install dependencies (this will include fuse.js for fuzzy searching):

    npm install

  2. Compile and run the server:

    npm start

Building the Docker Image

The Dockerfile handles installing all necessary dependencies.

docker build -t mcp/memory .

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Available Tools

9 tools
add_observationsC

Add new observations to existing entities in the knowledge graph

ParametersJSON Schema
NameRequiredDescriptionDefault
observationsYes

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is an 'Add' operation (implying mutation) but doesn't describe permissions needed, whether changes are reversible, rate limits, or what happens if entities don't exist. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.

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

Completeness2/5

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

For a mutation tool with 1 parameter (but complex nested structure), 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what observations are, how they're structured, what happens on success/failure, or provide any behavioral context needed for safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The tool description mentions 'observations' and 'existing entities' but doesn't explain the structure of observations, what 'entityName' refers to, or the format/constraints of observation contents. The description adds minimal value beyond what's implied by the parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Add new observations') and target ('to existing entities in the knowledge graph'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'create_entities' or 'delete_observations', which would require more specific context about when to use each.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'create_entities' (for new entities) or 'delete_observations'. It mentions 'existing entities' which implies a prerequisite, but offers no explicit when/when-not instructions or comparison with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_entitiesC

Create multiple new entities in the knowledge graph

ParametersJSON Schema
NameRequiredDescriptionDefault
entitiesYes

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Create multiple new entities', implying a write operation, but doesn't cover critical aspects like permissions required, whether creation is idempotent, error handling for duplicates, or rate limits. For a mutation tool with zero annotation coverage, this is a significant gap in transparency, leaving the agent to infer behavior from the name alone.

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 a single, efficient sentence that front-loads the key action and resource without unnecessary words. It avoids redundancy and gets straight to the point, making it easy to parse quickly. Every word earns its place, and there's no wasted verbiage, which is ideal for conciseness in tool descriptions.

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

Completeness2/5

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

Given the complexity of a batch creation tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't address return values, error conditions, or behavioral nuances like whether observations are validated. For a mutation operation in a knowledge graph context, more detail is needed to guide the agent effectively, making this inadequate for the tool's requirements.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning the schema provides no descriptions for parameters. The description mentions 'multiple new entities' but doesn't explain what 'entities' entail beyond the schema's structure (name, entityType, observations). It fails to add meaning, such as examples of entity types, format constraints for observations, or how batch processing works. With low coverage, the description doesn't compensate adequately, leaving parameters poorly documented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create') and the resource ('multiple new entities in the knowledge graph'), making the purpose immediately understandable. It distinguishes from siblings like 'delete_entities' or 'create_relations' by focusing on entity creation rather than deletion or relation management. However, it doesn't specify what constitutes an 'entity' beyond the schema, leaving some ambiguity compared to more detailed descriptions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'add_observations' or 'create_relations'. It doesn't mention prerequisites, such as whether entities must be unique or if there are limits on batch size, nor does it clarify when not to use it (e.g., for single entities or updates). This lack of context makes it harder for an agent to choose appropriately among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_relationsC

Create multiple new relations between entities in the knowledge graph. Relations should be in active voice

ParametersJSON Schema
NameRequiredDescriptionDefault
relationsYes

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It indicates a write operation ('Create') but doesn't disclose permissions, side effects, error handling, or response format. The active voice note is trivial and doesn't add meaningful operational context.

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 brief and front-loaded with the core purpose, but the second sentence about active voice adds little value and could be omitted. It's efficient but not perfectly optimized, warranting a 4.

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

Completeness2/5

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

For a write tool with no annotations, 0% schema coverage, and no output schema, the description is inadequate. It misses critical details like mutation effects, error cases, and return values, leaving significant gaps for agent understanding.

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 0%, so the description must compensate but only vaguely mentions 'relations' without explaining the structure or semantics. It implies batch creation but doesn't detail the 'relations' array or its items. Baseline 3 is given as it hints at parameters but lacks substantive clarification.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create multiple new relations') and the resource ('between entities in the knowledge graph'), distinguishing it from siblings like 'create_entities' or 'delete_relations'. However, it doesn't explicitly differentiate from 'add_observations' or other relation-related tools, keeping it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'add_observations' or 'create_entities', nor does it mention prerequisites or exclusions. The only contextual note is about active voice, which is stylistic rather than functional guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

delete_entitiesC

Delete multiple entities and their associated relations from the knowledge graph

ParametersJSON Schema
NameRequiredDescriptionDefault
entityNamesYesAn array of entity names to delete

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive mutation, it doesn't specify whether deletions are permanent, reversible, require specific permissions, affect downstream data, or have rate limits. The mention of 'associated relations' being deleted is useful but insufficient for a mutation tool.

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 a single, efficient sentence that states the core functionality without unnecessary words. It could be slightly improved by front-loading more critical information (like permanence warnings), but it's appropriately sized for its purpose.

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

Completeness2/5

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

For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks crucial information about behavioral consequences (permanence, side effects), authorization needs, error handling, and what happens to 'associated relations' (e.g., cascade behavior). The context signals indicate moderate complexity that warrants more disclosure.

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 100%, so the schema fully documents the 'entityNames' parameter. The description adds no additional parameter semantics beyond what's in the schema (e.g., format examples, constraints, or relationship to 'associated relations'). Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Delete') and target ('multiple entities and their associated relations from the knowledge graph'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'delete_observations' or 'delete_relations', which handle different resource types in the same system.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'delete_observations' or 'delete_relations', nor does it mention prerequisites, constraints, or typical use cases. It simply states what the tool does without contextual usage information.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

delete_observationsC

Delete specific observations from entities in the knowledge graph

ParametersJSON Schema
NameRequiredDescriptionDefault
deletionsYes

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool deletes observations, implying a destructive mutation, but doesn't describe consequences (e.g., whether deletions are permanent, reversible, or affect related data), permissions required, error handling, or rate limits. For a destructive tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence with zero waste. It's front-loaded with the core action and target, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.

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

Completeness2/5

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

Given the tool's complexity (destructive mutation with 1 parameter but nested structure), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects), parameter details, or return values, leaving critical gaps for safe and effective use by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description mentions 'specific observations from entities' but doesn't explain the input structure (e.g., that 'deletions' is an array of objects with 'entityName' and 'observations' fields). It adds minimal semantic value beyond what's inferred from the tool name, failing to compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Delete') and target ('specific observations from entities in the knowledge graph'), which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'delete_entities' by focusing on observations rather than entire entities. However, it doesn't explicitly contrast with 'add_observations' beyond the verb difference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing existing observations), exclusions, or comparisons to siblings like 'delete_entities' (for removing entire entities) or 'add_observations' (for adding vs. deleting). Usage is implied by the verb 'delete' but lacks explicit context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

delete_relationsC

Delete multiple relations from the knowledge graph

ParametersJSON Schema
NameRequiredDescriptionDefault
relationsYesAn array of relations to delete

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes relations but doesn't mention whether this is reversible, what permissions are required, how deletions affect the graph structure, or any rate limits/constraints. For a destructive operation with zero annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence with zero wasted words. It's appropriately sized for a tool with one primary parameter and clear schema documentation, making it easy to parse and understand at a glance.

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

Completeness2/5

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

For a destructive tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after deletion, whether there are side effects, what errors might occur, or how to verify success. Given the complexity of graph operations, more context is needed for safe and effective use.

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 100%, so the schema fully documents the 'relations' parameter and its nested structure. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Delete') and resource ('multiple relations from the knowledge graph'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'delete_entities' or 'delete_observations', which handle different resource types in the same system.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'delete_entities' or 'delete_observations', nor does it mention prerequisites, constraints, or appropriate contexts for deletion operations. Usage is implied by the tool name but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

open_nodesC

Open specific nodes in the knowledge graph by their names

ParametersJSON Schema
NameRequiredDescriptionDefault
namesYesAn array of entity names to retrieve

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'open[s] specific nodes' but doesn't clarify what 'open' entails operationally—whether it's a read-only retrieval, if it modifies state, requires permissions, or has rate limits. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that directly states the tool's purpose without redundancy. It is front-loaded and wastes no words, making it easy for an agent to parse quickly.

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

Completeness2/5

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

Given the tool's complexity (involving a knowledge graph) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'open' means in terms of behavior, return values, or potential side effects, leaving the agent with insufficient context for safe and effective use.

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?

The schema description coverage is 100%, with the parameter 'names' fully documented in the schema as 'An array of entity names to retrieve'. The description adds minimal value beyond this, mentioning 'by their names' but not elaborating on format, constraints, or examples. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('open') and target ('specific nodes in the knowledge graph'), specifying they are opened 'by their names'. It distinguishes from siblings like 'read_graph' (general reading) and 'search_nodes' (searching), though not explicitly. However, it lacks full sibling differentiation, as 'open' versus 'read' could be ambiguous without clarification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 is provided. The description implies usage for retrieving nodes by name, but it doesn't specify prerequisites, exclusions, or compare to siblings like 'read_graph' or 'search_nodes'. This leaves the agent without clear direction on tool selection in context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

read_graphC

Read the entire knowledge graph

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'read' (implying safe operation) but doesn't disclose behavioral traits: format of returned data (list, structure, size), whether it's paginated/streamed, performance characteristics, or authentication needs. For a zero-param tool that presumably returns substantial data, this is inadequate.

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?

Extremely concise (4 words) and front-loaded. Every word contributes: 'read' (action), 'entire' (scope), 'knowledge graph' (resource). No wasted sentences.

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

Completeness2/5

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

Given zero parameters but no output schema and no annotations, the description is incomplete. It doesn't explain what 'read' returns (e.g., nodes/edges list, serialized format) or behavioral aspects (performance, size limits). For a tool that likely returns complex graph data, more context is needed.

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?

The tool has zero parameters, and schema description coverage is 100% (empty schema). The description doesn't need to explain parameters, and 'entire knowledge graph' implicitly confirms no filtering parameters exist. Baseline for zero params is 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('read') and resource ('knowledge graph'), but is vague about scope ('entire' is ambiguous - does this mean all nodes/edges, or a complete dump?). It doesn't distinguish from sibling tools like 'search_nodes' or 'open_nodes' which might also read graph data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like 'search_nodes' or 'open_nodes'. The description implies this reads everything, but doesn't specify use cases (e.g., for analysis vs. specific lookups) or warn about performance with large graphs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_nodesA

Performs a fuzzy semantic search for nodes in the knowledge graph based on a query. Returns a list of matching entities, each with a confidence score from 0.0 to 1.0 (higher is better).

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query to match against entity names, types, and observation content.

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the search is 'fuzzy semantic' and returns confidence scores, which adds useful context. However, it lacks details on permissions, rate limits, pagination, or error handling, which are important for a search operation.

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 concise sentences with zero waste. It front-loads the purpose and efficiently covers key behavioral aspects (fuzzy semantic search, confidence scores) without unnecessary elaboration.

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 tool's moderate complexity (search operation with one parameter) and no annotations or output schema, the description is adequate but has gaps. It explains the core functionality and return format but lacks details on error cases, performance, or integration with sibling tools, making it minimally viable.

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 100%, so the schema already documents the single 'query' parameter. The description adds minimal value by implying the query matches against 'entity names, types, and observation content', but this is redundant with the schema's description. Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific action ('performs a fuzzy semantic search'), the target resource ('nodes in the knowledge graph'), and the scope ('based on a query'). It distinguishes itself from siblings like 'open_nodes' (likely for opening specific nodes) and 'read_graph' (likely for reading the entire graph) by focusing on search functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or comparisons to sibling tools like 'open_nodes' or 'read_graph', leaving the agent to infer usage context independently.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose targeting specific operations on the knowledge graph. For example, add_observations vs. delete_observations handle different lifecycle stages, while open_nodes and search_nodes serve distinct access patterns. There is no significant overlap that would cause agent misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun naming pattern using snake_case, such as create_entities, delete_relations, and search_nodes. The naming is predictable and readable throughout the set, with no deviations in style or convention.

Tool Count5/5

With 9 tools, the server is well-scoped for managing a knowledge graph, covering core operations like creation, deletion, reading, and searching. Each tool earns its place without being overly sparse or bloated, fitting typical expectations for this domain.

Completeness4/5

The tool set provides strong coverage for CRUD operations on entities, relations, and observations, along with search and read capabilities. A minor gap exists in update operations (e.g., no update_entities or update_relations), but agents can work around this by deleting and recreating as needed.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/flrngel/fuzzy-memory-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server