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rayana_ai_rankings

Read-only

Latest ranked candidate batch for a lane: daily (the main research lane), turtle (breakout system), or mr (mean-reversion). Rows come back in rank order, best first, so the default page is the top of the batch.

``total`` in the response is the batch size; when it exceeds ``count``,
page with ``offset``. ``include_assessment`` adds each row's technical
blob (pattern and cluster validation, volume profile) — it triples the
response, so prefer ``rayana_ai_ticker`` for one name's detail and leave
this off when scanning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
laneNodaily
limitNo
offsetNo
include_assessmentNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
laneYes
countYes
totalNo
asof_hintNo
truncatedNo
candidatesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, it discloses ordering (rank order, best first), pagination semantics (total vs count), and the performance cost of include_assessment. These are meaningful behavioral traits not visible in the schema or annotations.

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 compact, front-loaded with the core purpose, and every sentence adds operational value. No filler or redundant restatement of schema details.

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 an output schema exists and annotations cover read-only/open-world behavior, the description supplies the missing operational context: ranking order, pagination trigger, and when to use the expensive option. An agent has enough to call and interpret the result correctly.

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 description coverage is 0%, but the description compensates well by explaining lane values, offset-based paging, and include_assessment's effect. It does not explicitly define limit, though the total/count/offset explanation implies it, so a small gap remains.

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 states a specific verb and resource: returns the latest ranked candidate batch for a named lane, and enumerates the three valid lanes. It clearly differentiates this tool from sibling tools like rayana_ai_ticker by framing this as batch rankings rather than single-name detail.

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?

It gives explicit guidance on when to page via offset, when to leave include_assessment off, and when to prefer rayana_ai_ticker for single-name detail. This is actionable routing advice that helps an agent choose correctly.

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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