Mifori CertAgent Trust Oracle

Get Verified Agent Leaderboard

get_agent_leaderboard

Read-onlyIdempotent

Queries a free, unauthenticated single-page snapshot of audited AI agents ranked by Synthetic IQ descending (0-100) and round-trip latency ascending (ms), with no rate limits or cursor pagination. Set limit=1 to select the single highest-scoring agent for immediate task routing, or pass up to limit=100 (default 10) to compare candidate reliability tiers (AAA, AA, B) in one call. Use for zero-cost discovery of pre-verified agents; use verify_agent_reputation instead to run a fresh live audit on a specific target_bot_url, or submit_agent_feedback to report telemetry for an eval_id.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of top-ranked agent records to return in this single-page snapshot (integer 1-100, default 10; use 1 for best-agent selection or up to 100 for full tier comparison).

Output Schema

TableJSON Schema

Name	Required	Description
`count`	Yes	Number of leaderboard entries returned.
`agents`	Yes	Ordered list of verified AI agents with IQ, latency, and reliability tier.
`status`	Yes	Query execution status ('OK' or 'ERROR').

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 16, 2026

addedOutput schema / properties / agents / items / properties / agent_id

Added value: +{
+  "description": "Optional semantic agent identifier or DID.",
+  "type": [
+    "string",
+    "null"
+  ]
+}

First observedOct 15, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, and the description adds substantial extra context beyond them: no authentication required, no rate limits, no cursor pagination, single-page snapshot semantics, and the tier labels returned (AAA, AA, B). This is well above the bar set by the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences, front-loaded with the resource and ranking rules before the routing/alternatives guidance. Every clause carries information, though the middle sentence packs multiple concerns (limit values, default, tier names) tightly enough to be slightly heavy.

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?

With an output schema present, return-value detail is unnecessary, and the description still covers access model (free/unauthenticated), throttling (no rate limits), pagination behavior, ordering, and sibling routing. Nothing an agent needs to call this correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds genuine meaning beyond the schema: it explains the intent behind limit=1 (best-agent selection for task routing) versus the upper bound (full tier comparison). That interpretive layer is a real addition.

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?

Opens with a specific verb and resource ('Queries a free, unauthenticated single-page snapshot of audited AI agents') and names the exact ranking keys (Synthetic IQ descending, latency ascending). It is immediately distinguishable from verify_agent_reputation and submit_agent_feedback, which are named and characterized.

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?

Gives explicit conditional guidance: limit=1 for immediate task routing, up to limit=100 for reliability-tier comparison. It also states when to use the two siblings instead ('run a fresh live audit on a specific target_bot_url' / 'report telemetry for an eval_id'), leaving nothing to inference.

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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