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finstack-mcp

finstack-mcp

by finstacklabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

dividend_history

Retrieve historical dividend payments for any stock, including dates, amounts, totals, averages, and latest dividend info.

Instructions

Get historical dividend payments for a stock.

Returns past dividend dates and amounts, total dividends paid, average dividend, and latest dividend info.

Args: symbol: Stock ticker (e.g., ITC, COALINDIA, AAPL, MSFT)

Examples: dividend_history("ITC") → ITC dividend payment history dividend_history("COALINDIA") → Coal India dividends dividend_history("AAPL") → Apple dividend history

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "dividend_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.2

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the output data (historical dates, amounts, totals, average, latest), but doesn't mention potential errors, data coverage limitations, or any special behaviors. This is adequate but not thorough.

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

Conciseness4/5

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

The description is concise and front-loaded, starting with the main purpose and then providing return details and an Args block. The examples are slightly redundant but serve as useful usage illustrations. It's well-structured and not verbose.

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

Completeness4/5

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

For a low-complexity tool with one parameter and an output schema, the description covers the essential information: what it does, what it returns, and what the parameter means. It doesn't mention market coverage or edge cases, but the simplicity of the tool reduces the need for deeper context.

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?

The schema has 0% coverage for parameter descriptions, so the description's 'symbol: Stock ticker (e.g., ITC, COALINDIA, AAPL, MSFT)' adds essential meaning beyond the schema's bare string type. The examples reinforce the expected format, though more detail (e.g., exchange) could be added.

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 'Get historical dividend payments for a stock' and specifies the exact return data (dates, amounts, totals, average, latest). While this is specific, it does not explicitly distinguish from sibling tools like dividend_history_deep, so it doesn't fully earn 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 Guidelines3/5

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

Usage is implied through the Args section and examples (e.g., ITC, AAPL), but there is no explicit guidance on when to use this tool versus alternatives such as dividend_history_deep or stock_historical. The context signals and sibling list suggest overlaps but the description doesn't address them.

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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