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get_topic_tree

Analyze conversation topic structures from social media searches to visualize how topics and subtopics distribute across platforms like Twitter, Bluesky, and YouTube.

Instructions

Get the conversation topic tree for a keyword search. Shows how topics and subtopics are distributed across the search results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
search_idYesKeyword search ID
platformNoFilter by platform

Implementation Reference

  • The handler function for get_topic_tree tool.
    async (params) => {
      try {
        const query = params.platform ? `?platform=${params.platform}` : "";
        const data = await apiGet(`/iq/keyword_search/${params.search_id}/topic_tree${query}`);
        return { content: [{ type: "text", text: JSON.stringify(data, null, 2) }] };
      } catch (e) {
        return { content: [{ type: "text", text: String(e) }], isError: true };
      }
    }
  • Registration of the get_topic_tree tool.
    server.tool(
      "get_topic_tree",
      "Get the conversation topic tree for a keyword search. Shows how topics and subtopics are distributed across the search results.",
      {
        search_id: z.number().int().positive().describe("Keyword search ID"),
        platform: z
          .enum(["twitter", "bluesky", "youtube"])
          .optional()
          .describe("Filter by platform"),
      },
      async (params) => {
        try {
          const query = params.platform ? `?platform=${params.platform}` : "";
          const data = await apiGet(`/iq/keyword_search/${params.search_id}/topic_tree${query}`);
          return { content: [{ type: "text", text: JSON.stringify(data, null, 2) }] };
        } catch (e) {
          return { content: [{ type: "text", text: String(e) }], isError: true };
        }
      }
    );
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 what the tool does but lacks behavioral details like whether it's read-only, requires specific permissions, has rate limits, returns structured data, or involves pagination. For a tool with no annotation coverage, this is a significant gap.

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 two sentences with zero waste—each sentence adds value. It's front-loaded with the core purpose and efficiently explains the output's utility. No unnecessary words or repetition.

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

Completeness3/5

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 minimal but adequate for a read operation. It covers the purpose and output concept ('how topics and subtopics are distributed'), but lacks details on return format, error handling, or behavioral traits. For a tool with 2 parameters and 100% schema coverage, it's passable but not comprehensive.

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%, so the schema already documents both parameters (search_id and platform). The description adds context by linking parameters to the purpose ('for a keyword search'), but doesn't provide additional syntax or format details beyond what the schema offers. Baseline 3 is appropriate when schema does the heavy lifting.

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 verb ('Get') and resource ('conversation topic tree'), and specifies it's for a keyword search. It distinguishes from siblings like get_keyword_search_posts by focusing on topic distribution rather than raw posts. However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.

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 context ('for a keyword search') and hints at when to use it (to see topic distribution across results), but lacks explicit guidance on when to choose this over alternatives like get_keyword_search or get_keyword_search_posts. No exclusions or prerequisites are mentioned.

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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