VNStock MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return type (pd.DataFrame) and output format options, but doesn't cover critical aspects like rate limits, authentication needs, data freshness, error handling, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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

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

The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured Args and Returns sections. It avoids unnecessary fluff, though the formatting could be slightly more polished (e.g., using bullet points). Every sentence earns its place by conveying essential information.

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

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

Given no annotations, 0% schema coverage, and no output schema, the description is moderately complete. It covers purpose, parameters, and return type, but lacks details on behavioral traits, error cases, and deeper parameter semantics. For a financial data tool with 2 parameters, it's adequate but has clear gaps that could hinder effective use by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaning by specifying 'symbol: str' and 'output_format: Literal['json', 'dataframe'] = 'json'', explaining parameter types and default values. However, it doesn't clarify what 'symbol' represents (e.g., stock ticker), valid formats, or constraints, leaving some ambiguity. With 0% coverage, this provides basic but incomplete semantic context.

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

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

The description clearly states the tool's purpose: 'Get company dividends from stock market' specifies the verb ('Get') and resource ('company dividends'), distinguishing it from siblings like get_balance_sheets or get_income_statements. However, it doesn't explicitly differentiate from tools like get_company_events or get_company_trading_stats that might also involve company data, so it's not fully sibling-distinctive.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., for financial analysis), or compare to siblings like get_company_ratio_summary or get_company_reports. Usage is implied by the purpose but lacks explicit when/when-not instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.