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list_knowledge_lists

Discover available knowledge lists with field definitions, row counts, and metadata to understand data structure before querying.

Instructions

List all knowledge list schemas with field definitions, row counts, and metadata. Returns detailed information about each list including fields, source type, category, entity config, and KG sync status. Use this to discover what lists exist and understand their structure before querying rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The MCP tool handler that calls the client's listKnowledgeLists method.
    async (_args, extra) => {
        const client = clientFactory(extra);
        const result = await client.listKnowledgeLists();
        return {
            content: [{
                type: 'text' as const,
                text: JSON.stringify(result, null, 2),
            }],
        };
    }
  • The actual client method that makes the API request to fetch knowledge lists.
    async listKnowledgeLists() {
        return this.request('/knowledge/lists');
    }
  • The MCP tool registration for list_knowledge_lists.
        server.tool(
            'list_knowledge_lists',
            `List all knowledge list schemas with field definitions, row counts, and metadata.
    Returns detailed information about each list including fields, source type, category, entity config, and KG sync status.
    Use this to discover what lists exist and understand their structure before querying rows.`,
            {},
            async (_args, extra) => {
                const client = clientFactory(extra);
                const result = await client.listKnowledgeLists();
                return {
                    content: [{
                        type: 'text' as const,
                        text: JSON.stringify(result, null, 2),
                    }],
                };
            }
        );
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the tool's behavior by stating it returns detailed information (fields, source type, etc.) and its purpose for discovery, but lacks details on potential limitations like pagination, rate limits, or authentication requirements. The description doesn't contradict annotations (none exist), but could be more comprehensive for a tool with no annotation support.

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 front-loaded with the core purpose in the first sentence, followed by elaboration and usage guidance. All three sentences add value: the first states what it does, the second details the returned information, and the third explains when to use it. There is no wasted text, making it efficient and well-structured.

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

Completeness4/5

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

Given the tool has 0 parameters, no annotations, and no output schema, the description does a good job of explaining the tool's purpose, behavior, and usage. It covers what the tool returns and why to use it, but could be more complete by mentioning potential output format or limitations. For a simple list tool with no structured metadata, it's largely adequate.

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 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter semantics, but it implicitly confirms no parameters are needed by not mentioning any. This aligns with the schema, earning a baseline score above 3 due to the absence of parameters.

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 tool's purpose with specific verbs ('List all knowledge list schemas') and resources ('knowledge list schemas'), distinguishing it from siblings like 'get_knowledge_rows' or 'get_knowledge_text' by focusing on metadata rather than content. It explicitly mentions what information is returned (field definitions, row counts, metadata) and why to use it (to discover lists and understand structure).

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool ('to discover what lists exist and understand their structure before querying rows'), implying it should be used as a precursor to tools like 'get_knowledge_rows'. It distinguishes from siblings by focusing on schema discovery rather than data retrieval or other operations, though it doesn't explicitly state 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.

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