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get_agent_reputation

Retrieve community reputation data for ACP agents, including reviews, ratings, sentiment analysis, and market consensus to assess agent credibility within the maiat-protocol ecosystem.

Instructions

Get community reputation for an ACP agent — reviews, average rating, sentiment analysis, and market consensus. Use this alongside get_agent_trust for a complete picture: behavioral data + community intelligence. After acting on this data, report the outcome via report_outcome to earn 5 Scarab.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYesEthereum/Base wallet address (0x...) of the agent

Implementation Reference

  • The MCP tool handler for 'get_agent_reputation', which calls 'sdk.deep(address)' to retrieve reputation data.
    async ({ address }) => {
      try {
        const data = await sdk.deep(address);
        return {
          content: [
            {
              type: "text" as const,
              text: JSON.stringify(data, null, 2),
            },
          ],
        };
  • Registration of the 'get_agent_reputation' tool in the MCP server.
    server.tool(
      "get_agent_reputation",
      "Get community reputation for an ACP agent — reviews, average rating, sentiment analysis, and market consensus. Use this alongside get_agent_trust for a complete picture: behavioral data + community intelligence. After acting on this data, report the outcome via report_outcome to earn 5 Scarab.",
      {
        address: z
          .string()
          .describe("Ethereum/Base wallet address (0x...) of the agent"),
      },
  • The underlying function implementation for fetching agent reputation from the blockchain, although the MCP tool uses a different 'sdk.deep' method for this specific tool name.
    export async function getAgentReputation(
      agentId: bigint
    ): Promise<{ count: number; normalizedScore: number } | null> {
      return withFallback(async (client) => {
        const [reputation, normalizedScore] = await Promise.all([
          client.readContract({
            address: REPUTATION_REGISTRY,
            abi: REPUTATION_ABI,
            functionName: 'getReputation',
            args: [agentId],
          }),
          client.readContract({
            address: REPUTATION_REGISTRY,
            abi: REPUTATION_ABI,
            functionName: 'getReputationNormalized',
            args: [agentId],
          }),
        ])
    
        const [count] = reputation as [bigint, bigint]
    
        return {
          count: Number(count),
          normalizedScore: Number(normalizedScore as bigint),
        }
      })
    }
Behavior3/5

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 tool returns reputation data but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or error conditions. The description adds value by mentioning the reward incentive ('earn 5 Scarab') but lacks operational details.

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 efficiently structured in two sentences: first states purpose and data returned, second provides usage guidelines and workflow integration. Every phrase adds value with zero wasted words, and key information is front-loaded.

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's moderate complexity (single parameter, no output schema, no annotations), the description is reasonably complete. It explains what data is returned, how it fits with other tools, and the incentive for reporting outcomes. However, without an output schema, more detail on return format would be helpful.

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 the single 'address' parameter. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., format examples, validation rules, or edge cases). 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.

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 ('Get community reputation') and resources ('ACP agent'), listing the exact data returned (reviews, average rating, sentiment analysis, market consensus). It distinguishes from sibling 'get_agent_trust' by specifying this provides 'community intelligence' versus 'behavioral data'.

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?

Explicit guidance is provided on when to use this tool: 'Use this alongside get_agent_trust for a complete picture' and 'After acting on this data, report the outcome via report_outcome to earn 5 Scarab.' This gives clear context about complementary tools and workflow sequencing.

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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