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mainstreet_top_raters

Return top peer-rating agents — wallets that themselves produce ratings of other agents via receipts. ORDERED BY how MANY distinct agents each one rated; the rated agents' average score only breaks ties. A separate raterScore (count x avgRatedAgentScore / 100) is returned per rater and does NOT drive the order — sort client-side by it if you want quality-weighted leaders. Note: agents we have never scored count as 0 in that average; each row carries ratedAgentsScored / ratedAgentsUnscored so you can tell a bad rater from a gap in our coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 20).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations to lean on, the description carries the full burden and does so thoroughly. It discloses the exact ordering rule, tie-breaking rule, the raterScore formula, that unscored agents count as zero, and that the ratedAgentsScored/ratedAgentsUnscored fields let the caller distinguish a genuinely bad rater from a coverage 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 dense but every sentence earns its place: definition, ordering semantics, scoring formula, client-side sorting advice, and a caveat about zero-scored agents. It front-loads the core purpose and then layers necessary behavioral detail without fluff.

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

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema and no annotations, the description is remarkably complete. It explains what rows contain, how ordering works, how raterScore is computed, and which fields resolve potential ambiguity. An agent has enough information to call the tool and interpret its results correctly.

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 only parameter is `limit`, and its schema description already covers meaning, range, and default. The description does not need to repeat this; baseline 3 is appropriate since schema coverage is 100%.

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 opens with a specific verb and resource: 'Return top peer-rating agents' and clarifies what counts as one ('wallets that themselves produce ratings of other agents via receipts'). This makes the tool's purpose unmistakable and distinguishes it from sibling ranking tools like mainstreet_top_buyers and mainstreet_leaderboard.

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 clearly explains the default ordering behavior and explicitly tells the agent to sort client-side by raterScore when quality-weighted leaders are desired. It does not name alternative sibling tools or list exclusions, but it gives enough contextual guidance for an agent to decide how to use the result.

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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