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yfinance-ai MCP server

yfinance-ai MCP server

by lkcair
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_complete_analysis

Read-onlyIdempotent

Run a full stock analysis using all available Yahoo Finance functions, with optional peer comparisons, to return a comprehensive company overview.

Instructions

Run a complete analysis of a stock by calling ALL available functions. This is the most comprehensive analysis available - use when user asks for "everything about" or "complete analysis of" a company.

Args: ticker: Stock ticker symbol (e.g., 'AAPL', 'MSFT') peers: Optional comma-separated peer tickers for comparison (e.g., 'MSFT,GOOGL,META') AI should provide contextually relevant peers based on the conversation.

Returns: Comprehensive analysis with results from all applicable functions

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": {
      "ticker": "<ticker>"
    },
    "name": "get_complete_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
peersNo
tickerYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already carry readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds that this is a fan-out call invoking every function, but does not disclose the practical consequence an agent needs: latency, cost, or partial-failure behavior when some sub-calls fail.

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?

Front-loaded with the tool's nature and trigger condition, then Args and Returns in a scannable layout. The Returns block is slightly redundant given an output schema exists, but nothing is padded.

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 two-parameter aggregator with an output schema, the description covers purpose, trigger, and both parameters adequately, so returns needn't be spelled out. The remaining gap is operational: how the agent should interpret or handle a partial aggregation result is unaddressed.

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 has to carry both parameters, and it does: ticker is documented with concrete format examples ('AAPL', 'MSFT') and peers is documented with format plus a behavioral instruction to choose contextually relevant peers from the conversation. That compensates well for the empty schema descriptions, though it does not say what happens when peers is omitted or empty.

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

Purpose5/5

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

The description states a specific verb and resource ('Run a complete analysis of a stock') and defines its distinguishing behavior explicitly: it calls ALL available functions, making it the aggregation tool rather than any single-indicator sibling. An agent can tell it apart from get_financial_summary or get_peer_comparison without opening a schema.

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

Usage Guidelines4/5

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

It gives a clear trigger phrase ('everything about' / 'complete analysis of' a company), which is a concrete when-to-use signal. It stops short of saying when NOT to use it or pointing to narrower alternatives (e.g., get_peer_comparison for comparison-only requests), so usage is clear but not exhaustively routed.

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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