Rayana AI

rayana_ai_ticker_price

Read-only

Daily close-price history for one ticker over the given period.

Long periods are evenly sampled down to ``max_points`` bars rather than
cut short, so the series still spans the whole period — ``5Y`` and ``MAX``
return the shape of the history, not its last year. ``last_close``,
``change_pct`` and the ``range_low``/``range_high`` extremes are computed
over every bar regardless of sampling.

Input Schema

TableJSON Schema

Name	Required	Default
`period`	No	1Y
`symbol`	Yes
`max_points`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`period`	Yes
`points`	Yes
`ticker`	Yes
`range_low`	No
`change_pct`	No
`last_close`	No
`range_high`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 17, 2026

TDQS

A4/5.0

Behavior5/5

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

The description adds significant non-obvious behavior: long periods are evenly sampled down to max_points rather than cut short, 5Y/MAX return the full shape, and metrics like last_close/change_pct are computed over every bar regardless of sampling. This goes well beyond the readOnlyHint and openWorldHint annotations and is consistent with them.

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 and front-loaded: the core purpose is stated in the first sentence, and the second sentence justifies the sampling behavior without excess prose. Every sentence adds useful information.

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 the read-only annotations, an output schema, and only one required parameter, the description covers the necessary call semantics, parameter meaning, and hidden sampling behavior. Nothing material is missing for an agent to invoke this 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%, so the description must carry the meaning. It does explain max_points and period behavior ('Long periods are evenly sampled down to max_points bars') and references period values like 5Y and MAX. Symbol is self-explanatory, and the enum/defaults are already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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

The description clearly identifies the resource and action: 'Daily close-price history for one ticker over the given period.' It is specific enough to distinguish from broker/analyst or article siblings, though it does not name a sibling or explicitly contrast itself with rayana_ai_ticker.

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

Usage Guidelines2/5

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

No guidance is given about when to prefer this tool over siblings or when not to use it. The description explains behavior but never states conditions like 'use this for historical price series' or 'for current quotes, use X instead.'

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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