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get_knowledge_text

Fetch text-based knowledge entries like feedback files, notes, or configuration text from the workspace by specifying a key to access stored information.

Instructions

Fetch a text-type knowledge entry by key. Use this to access text-based knowledge like feedback files, notes, or configuration text stored in the workspace.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesThe key of the text entry to fetch

Implementation Reference

  • Tool handler for 'get_knowledge_text' registered in the MCP server. It calls the client's getKnowledgeText method.
    server.tool(
        'get_knowledge_text',
        `Fetch a text-type knowledge entry by key. Use this to access text-based knowledge like feedback files, notes, or configuration text stored in the workspace.`,
        {
            key: z.string().describe('The key of the text entry to fetch'),
        },
        async ({ key }, extra) => {
            const client = clientFactory(extra);
            const result = await client.getKnowledgeText(key);
            return {
                content: [{
                    type: 'text' as const,
                    text: JSON.stringify(result, null, 2),
                }],
            };
        }
    );
  • The underlying client implementation that makes the HTTP request to the /knowledge/text API endpoint.
    async getKnowledgeText(key: string) {
        const query = new URLSearchParams({ key });
        return this.request(`/knowledge/text?${query}`);
    }
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. It mentions fetching text entries but does not disclose behavioral traits such as authentication requirements, rate limits, error handling, or whether it's a read-only operation. The description is minimal and misses key operational details needed for safe and effective use.

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 two sentences, front-loaded with the core purpose and followed by usage context. It is efficient with no wasted words, though it could be slightly more structured (e.g., separating purpose from examples).

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. It lacks information on return values, error cases, permissions, or system behavior. For a tool with one parameter and high schema coverage, the description does not compensate for missing contextual details needed for full 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?

The input schema has 100% description coverage, with the 'key' parameter documented. The description adds minimal semantics by specifying that the key is for 'text-type knowledge entry' and examples like 'feedback files, notes, or configuration text', but does not provide additional details beyond what the schema already covers. Baseline is 3 due to high schema coverage.

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 ('fetch') and resource ('text-type knowledge entry by key'), and specifies the type of content ('text-based knowledge like feedback files, notes, or configuration text'). However, it does not explicitly differentiate from sibling tools like 'get_knowledge_rows' or 'list_knowledge_lists', which might handle similar knowledge data but in different formats or scopes.

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

Usage Guidelines3/5

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

The description implies usage for accessing text-based knowledge stored in the workspace, but does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'get_knowledge_rows' for non-text entries or 'list_knowledge_lists' for listing). It offers some context but lacks clear exclusions or named alternatives.

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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