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axionquant

AxionQuant MCP Server

Official
by axionquant

financials_stockholders_equity

Get historical stockholders equity for a stock ticker, with optional number of periods, using live market data.

Instructions

Get historical stockholders equity

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker
periodsNoNumber of periods
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only says 'Get historical stockholders equity' which implies a read-only query but doesn't disclose the return format, time-series structure, data granularity, or any potential limitations. For a simple data retrieval tool this is minimal, but it doesn't contradict anything and offers some implicit safety (historical data retrieval is inherently non-destructive).

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

Conciseness3/5

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

The description is extremely short (4 words), which is concise, but it sacrifices necessary detail. It is front-loaded with the core action and resource, but there is no structure or additional context. This is not a well-structured description; it is merely a minimal phrase. It's appropriately sized for the simplicity but lacks the informative structure that would earn a higher score.

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?

Given the low complexity (2 parameters, no output schema), the description is still incomplete. It does not explain the nature of the returned data (e.g., list of values over time), the units, or the meaning of 'periods' (e.g., quarters, years). While siblings follow a similar minimal pattern, the description alone does not fully equip an agent to understand what to expect from the call. There is room for more context without being verbose.

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% as both parameters ('ticker' and 'periods') have descriptions in the input schema. The description adds no semantic meaning beyond what the schema already documents, so the baseline of 3 is appropriate. The description doesn't elaborate on how the parameters interact or the expected format of the 'periods' argument, but the schema already covers basic meaning.

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 states the verb 'Get' and the resource 'historical stockholders equity', making the purpose unambiguous. However, it does not differentiate from the many sibling financials_* tools (e.g., financials_revenue, financials_net_income) which follow the identical pattern 'Get historical <metric>'. Without further context, an agent could confuse it with other financial history tools, so it lacks sibling differentiation but is otherwise clear.

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 provided on when to use this tool versus the many alternative financial data tools. There is no mention of specific scenarios, prerequisites, or exclusions. The description is silent on usage context, leaving the agent to infer based solely on the name and metric.

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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