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FinanceKit MCP

FinanceKit MCP

by vdalhambra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

correlation_matrix

Read-only

Compute pairwise correlations between assets to assess portfolio diversification. Identify which assets move together or apart using Pearson correlation.

Instructions

Compute correlation matrix between multiple assets.

Returns pairwise Pearson correlations. Useful for portfolio diversification analysis — lower correlations = better diversification. Correlations near 1 = assets move together (less diversification benefit).

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": {
      "symbols": "<symbols>"
    },
    "name": "correlation_matrix"
  }
}

Input Schema

NameRequiredDescriptionDefault
periodNoPeriod: 3mo, 6mo, 1y, 2y6mo
symbolsYesComma-separated tickers (e.g., 'AAPL,MSFT,GOOGL,SPY')

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.0
  2. Removedv1.2.0
  3. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true. The description adds that it computes Pearson correlations and provides interpretation (lower vs higher correlations), which is useful behavioral context beyond the annotation.

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?

Three sentences, front-loaded with the action, no wasted words. Each sentence adds value: action, return type, and interpretation advice.

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?

Given the output schema exists, the description adequately explains the return (pairwise Pearson correlations) and its interpretation. Missing details like data requirements (at least 2 symbols) are covered by the schema.

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 coverage is 100%, so description does not need to add parameter details. It adds no extra meaning beyond what schema already provides, meeting the baseline.

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?

Description clearly states 'Compute correlation matrix between multiple assets' and specifies it returns pairwise Pearson correlations. Differentiates from siblings like compare_assets and portfolio_analysis by focusing on correlation computation.

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?

Indicates usefulness for portfolio diversification analysis, but does not explicitly state when not to use or compare with alternatives like compare_assets or risk_metrics. Implied usage but no exclusions.

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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