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sharozdawa

ai-visibility-mcp

list_platforms

Discover supported AI platforms and understand how each sources and displays brand information for visibility tracking.

Instructions

List all supported AI platforms with details about how each sources and presents brand information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The list_platforms tool handler which returns a list of tracked AI platforms.
    // Tool: list_platforms
    server.tool(
      "list_platforms",
      "List all supported AI platforms with details about how each sources and presents brand information.",
      {},
      async () => {
        const output = {
          platforms: PLATFORMS.map((p) => ({
            id: p.id,
            name: p.name,
            company: p.company,
            description: p.description,
            sourceMethod: p.sourceMethod,
          })),
          totalPlatforms: PLATFORMS.length,
          note: "These are the AI platforms tracked by the AI Visibility tool. Each platform has different methods for sourcing brand information, which affects visibility strategies.",
        };
    
        return {
          content: [
            {
              type: "text" as const,
              text: JSON.stringify(output, 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 full burden. It mentions the output includes 'details about how each sources and presents brand information', which adds some behavioral context about the return format. However, it doesn't disclose critical traits like whether this is a read-only operation, potential rate limits, authentication needs, or pagination behavior for a list operation.

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 a single, efficient sentence that front-loads the core action ('List all supported AI platforms') and adds necessary detail about the output. Every word earns its place with zero redundancy or fluff.

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 the tool's simplicity (0 parameters, no annotations, no output schema), the description is adequate but has gaps. It explains what the tool returns but lacks behavioral context (e.g., safety, performance). For a list operation with no structured output schema, more detail on return format would help, though the mention of 'details' provides some guidance.

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 absence of inputs. The description appropriately doesn't waste space on parameters, maintaining focus on the tool's purpose. Baseline for 0 parameters is 4, as no parameter semantics are needed.

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 ('List') and resource ('supported AI platforms'), and specifies the scope ('with details about how each sources and presents brand information'). It distinguishes from siblings by focusing on platform metadata rather than brand analysis operations. However, it doesn't explicitly contrast with specific sibling tools, 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 Guidelines2/5

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

The description provides no guidance on when to use this tool versus the sibling tools. While it implies this is for platform discovery rather than brand checking, it doesn't specify scenarios, prerequisites, or alternatives. The agent must infer usage from the purpose alone.

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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