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axionquant

AxionQuant MCP Server

Official
by axionquant

financials_pe

Retrieve price-to-earnings ratio for any stock ticker. Specify date range and resampling frequency to track valuation changes over time.

Instructions

Get price-to-earnings ratio for a ticker

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd date (YYYY-MM-DD)
fromNoStart date (YYYY-MM-DD)
frameNoResampling frequency: daily, weekly, monthly
tickerYesStock ticker
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Get price-to-earnings ratio' without mentioning whether it returns a single value or a time series, how missing data is handled, or any output format. Given the presence of from/to/frame parameters, the return shape is ambiguous, which is a significant 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 a single, direct sentence with zero fluff. It is front-loaded with the purpose and uses no unnecessary words or repetition. This is ideal conciseness, though it sacrifices completeness—which is captured under other dimensions.

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

Completeness2/5

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

The tool has 4 parameters, no annotations, and no output schema. The description is too sparse to fully inform an agent—it doesn't explain whether the result is a single ratio or a series, how date filtering works, or what the response structure is. An agent could call it incorrectly (e.g., expecting a scalar when it returns a list). Significant information is missing.

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 each parameter already has a basic description. The tool description adds no extra meaning about parameter relationships or usage—it doesn't clarify that from/to are likely used together or that frame controls resampling. The schema carries the load, so a baseline 3 is appropriate.

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 states a clear verb and resource: 'Get price-to-earnings ratio for a ticker'. It accurately reflects the tool's function and is unambiguous. While it does not explicitly distinguish from sibling financial tools, the name and description make the purpose self-evident, so it earns a 4 rather than a 5.

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 on when to use this tool versus alternatives like financials_metrics or financials_snapshot. The description only states what it does, not the conditions or context in which it should be selected. This leaves the agent to infer usage from the name alone.

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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