Skip to main content
Glama
BRO3886

Memory Custom

by BRO3886

read_graph

Retrieve the complete knowledge graph from Memory Custom's MCP server to access structured data stored in memory files.

Instructions

Read the entire knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memoryFilePathYesThe path to the memory file

Implementation Reference

  • The core handler function in KnowledgeGraphManager that sets the memory file path and loads/returns the entire KnowledgeGraph from the file.
    async readGraph(filepath: string): Promise<KnowledgeGraph> {
      await this.setMemoryFilePath(filepath);
      return this.loadGraph();
    }
  • The input schema and metadata for the read_graph tool, defined in the tools list returned by the ListTools handler.
      name: "read_graph",
      description: "Read the entire knowledge graph",
      inputSchema: {
        type: "object",
        properties: {
          memoryFilePath: {
            type: "string",
            description: "The path to the memory file",
          },
        },
        required: ["memoryFilePath"],
      },
    },
  • index.ts:677-691 (registration)
    The dispatch case in the CallToolRequest handler that registers and executes the read_graph tool by calling the handler function.
    case "read_graph":
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              await knowledgeGraphManager.readGraph(
                args.memoryFilePath as string
              ),
              null,
              2
            ),
          },
        ],
      };
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 'Read' which implies a read-only operation, but doesn't address critical aspects like performance implications of reading 'entire' graphs, potential memory issues, authentication needs, error conditions, or return format. For a tool with no annotation coverage, this is a significant gap in 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—a single four-word sentence that's front-loaded with the core action. There's zero wasted language, and it efficiently communicates the basic purpose without unnecessary elaboration. This represents ideal conciseness for a simple tool.

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 apparent complexity (reading 'entire' knowledge graphs), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'reading' entails (e.g., returns structure, format, size limitations), doesn't address performance or memory concerns, and provides no context about the relationship between the memory file and the graph. For a potentially resource-intensive operation, this leaves too many questions unanswered.

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%, with the single parameter 'memoryFilePath' well-documented in the schema. The description adds no parameter-specific information beyond what the schema provides—it doesn't explain what a 'memory file' is, how it relates to the knowledge graph, or provide context about the file path format. Baseline 3 is appropriate when the schema does the heavy lifting.

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 'Read the entire knowledge graph' clearly states the verb ('Read') and resource ('knowledge graph'), but it's vague about scope and specificity. It doesn't distinguish from sibling tools like 'search_nodes' or 'open_nodes', which also likely read graph data. The phrase 'entire' adds some scope but remains ambiguous about what constitutes 'entire' versus partial reads.

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. With siblings like 'search_nodes' and 'open_nodes' that likely read graph data in different ways, the description offers no context on usage scenarios, prerequisites, or exclusions. This leaves the agent guessing about the appropriate context for this tool.

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

Install Server

Other Tools

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/BRO3886/mcp-memory-custom'

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