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YuNaga224

Obsidian Memory MCP

by YuNaga224

read_graph

Retrieve the complete knowledge graph from Obsidian Memory MCP to visualize AI memories as interconnected entities and relationships in Markdown format.

Instructions

Read the entire knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the 'read_graph' tool within the CallToolRequestSchema request handler's switch statement. Executes by calling storageManager.readGraph() and returning the JSON-serialized knowledge graph.
    case "read_graph":
      return { content: [{ type: "text", text: JSON.stringify(await storageManager.readGraph(), null, 2) }] };
  • Input schema definition for the 'read_graph' tool, registered in the tools list returned by the ListToolsRequestSchema handler. Takes no input parameters.
    {
      name: "read_graph",
      description: "Read the entire knowledge graph",
      inputSchema: {
        type: "object",
        properties: {},
      },
    },
  • Core helper method 'readGraph' in MarkdownStorageManager class that loads and returns the full KnowledgeGraph by delegating to the private loadGraph() method.
    async readGraph(): Promise<KnowledgeGraph> {
      return this.loadGraph();
    }
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 mentions 'Read' but doesn't disclose behavioral traits such as whether this is a safe operation, potential rate limits, authentication needs, or what format the output takes (e.g., JSON, size limits). This leaves significant gaps for an agent to understand how to use it effectively.

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, clear sentence with no wasted words. It's front-loaded and efficiently conveys the core action, making it easy 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 complexity implied by 'knowledge graph' and lack of annotations or output schema, the description is incomplete. It doesn't explain what 'read' returns (e.g., structure, size), how it interacts with other tools, or any prerequisites. This is inadequate for a tool that might handle large or complex data.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it adequately handles the lack of parameters without introducing confusion.

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 a clear action ('Read') and target ('the entire knowledge graph'), but it's vague about what 'read' entails—does it return all nodes, edges, metadata, or something else? It doesn't distinguish from siblings like 'search_nodes' or 'open_nodes', which might also involve reading 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 is provided on when to use this tool versus alternatives. For example, it doesn't explain if this is for bulk retrieval versus targeted searches (like 'search_nodes') or if it has performance implications. The description lacks context for choosing among sibling tools.

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

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