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read_graph

Access the complete knowledge graph to retrieve entities and relationships, enabling structured reasoning and memory for AI-assisted problem-solving within MCP Think Tank.

Instructions

Read the entire knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dummyNoPlaceholder parameter - this tool doesn't require parameters but returns the complete knowledge graph with entities and relationships

Implementation Reference

  • Handler function for the 'read_graph' tool that serializes and returns the entire knowledge graph as JSON.
        // Return as string - still using the underlying graph for compatibility
        return JSON.stringify(graph.toJSON());
      }
    });
  • Input schema definition for the 'read_graph' tool using Zod, featuring an optional dummy parameter.
      dummy: z.string().describe("Placeholder parameter - this tool doesn't require parameters but returns the complete knowledge graph with entities and relationships").optional()
    }),
    execute: async () => {
  • Registration of the 'read_graph' tool on the FastMCP server instance.
      name: 'read_graph',
      description: 'Read the entire knowledge graph',
      parameters: z.object({
        dummy: z.string().describe("Placeholder parameter - this tool doesn't require parameters but returns the complete knowledge graph with entities and relationships").optional()
      }),
      execute: async () => {
        // Return as string - still using the underlying graph for compatibility
        return JSON.stringify(graph.toJSON());
      }
    });
Behavior2/5

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

With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions 'entire knowledge graph' but doesn't disclose performance implications (e.g., may be slow for large graphs), output format, or error conditions. The placeholder parameter 'dummy' is unexplained in the description, leaving the agent guessing about its purpose.

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 function without fluff. However, it could be more front-loaded with critical details (e.g., 'Read all entities and relationships from the knowledge graph') to improve clarity. It earns its place but misses optimization opportunities.

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 no annotations and no output schema, the description is incomplete for a tool that presumably returns complex graph data. It doesn't explain what 'entire' includes (e.g., nodes, edges, properties), potential size limits, or return structure. For a read operation with undefined output, this leaves significant gaps for the agent.

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?

Schema description coverage is 100%, with the single parameter 'dummy' documented as a placeholder that doesn't require input. The description doesn't add parameter details beyond the schema, but since there are effectively 0 functional parameters, the baseline is 4. No compensation is needed for gaps.

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 lacks specificity about what constitutes 'entire' - does it return all nodes, edges, metadata, or a summary? It doesn't clearly distinguish from sibling tools like 'search_nodes' or 'memory_query' which might also retrieve graph information. The purpose is understandable but vague in scope.

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 like 'search_nodes' or 'memory_query'. The description implies it retrieves everything, but doesn't specify use cases (e.g., for overviews, bulk analysis) or warn against misuse (e.g., for large graphs). This leaves the agent without context for tool selection.

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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