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vote_review

Submit votes on reviews based on firsthand experience to validate accuracy and reward reviewers with Scarab tokens.

Instructions

Upvote or downvote a review. Only vote when you have firsthand experience with the reviewed entity. Upvotes reward the reviewer +2 Scarab.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reviewIdYesThe review ID to vote on
voteYes'up' if the review matches your experience, 'down' if it contradicts on-chain data

Implementation Reference

  • The handler function for the vote_review tool, which makes a request to the SDK to process the vote.
    async ({ reviewId, vote }) => {
      try {
        const data = await (sdk as any).request("/api/v1/review/vote", {
          method: "POST",
          body: JSON.stringify({ reviewId, vote }),
        });
        return { content: [{ type: "text" as const, text: JSON.stringify(data, null, 2) }] };
      } catch (err) {
        return { content: [{ type: "text" as const, text: JSON.stringify({ error: err instanceof Error ? err.message : String(err), reviewId }) }] };
      }
    }
  • Input validation schema for the vote_review tool.
    {
      reviewId: z.string().describe("The review ID to vote on"),
      vote: z.enum(["up", "down"]).describe("'up' if the review matches your experience, 'down' if it contradicts on-chain data"),
    },
  • Registration of the vote_review tool within the MCP server.
    server.tool(
      "vote_review",
      "Upvote or downvote a review. Only vote when you have firsthand experience with the reviewed entity. Upvotes reward the reviewer +2 Scarab.",
      {
        reviewId: z.string().describe("The review ID to vote on"),
        vote: z.enum(["up", "down"]).describe("'up' if the review matches your experience, 'down' if it contradicts on-chain data"),
      },
      async ({ reviewId, vote }) => {
        try {
          const data = await (sdk as any).request("/api/v1/review/vote", {
            method: "POST",
            body: JSON.stringify({ reviewId, vote }),
          });
          return { content: [{ type: "text" as const, text: JSON.stringify(data, null, 2) }] };
        } catch (err) {
          return { content: [{ type: "text" as const, text: JSON.stringify({ error: err instanceof Error ? err.message : String(err), reviewId }) }] };
        }
      }
    );
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that voting has consequences ('Upvotes reward the reviewer +2 Scarab'), which is valuable behavioral context. However, it doesn't disclose other important traits like whether this is a write operation (implied but not stated), authentication requirements, rate limits, or what happens with downvotes.

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 perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, and the second provides important usage guidance and behavioral context. There's zero wasted text, and the information is front-loaded effectively.

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?

For a 2-parameter mutation tool with no annotations and no output schema, the description provides adequate but incomplete context. It covers the purpose, usage conditions, and some behavioral consequences, but doesn't address what the tool returns, error conditions, or other operational details that would be helpful for an agent invoking this tool.

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?

With 100% schema description coverage, the schema already documents both parameters thoroughly. The description adds some context about the vote parameter ('up' if the review matches your experience, 'down' if it contradicts on-chain data'), but this mostly reinforces what's already in the schema's enum descriptions. The baseline of 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Upvote or downvote') and resource ('a review'), distinguishing it from sibling tools like submit_review (which creates reviews) or get_agent_reputation (which reads data). It provides a precise verb+resource combination that leaves no ambiguity about the tool's function.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool ('Only vote when you have firsthand experience with the reviewed entity'), which helps the agent understand appropriate usage scenarios. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools, preventing a perfect score.

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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