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fred-economic-intelligence-mcp

fred-economic-intelligence-mcp

by prithvi1029
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

calculate_growth_rate

Compute absolute and percentage changes for economic time series from FRED. Select comparison by calendar date to avoid off-by-one errors from missing values.

Instructions

Calculate absolute and percentage change for a FRED series.

The comparison observation is selected by calendar date rather
than only by list position, which avoids off-by-one errors when
observations contain missing values.

Examples:
CPIAUCSL with periods=12 for year-over-year change.
GDP with periods=1 for quarter-over-quarter change.
UNRATE with periods=1 for month-over-month change.

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": {
      "series_id": "<series_id>"
    },
    "name": "calculate_growth_rate"
  }
}

Input Schema

NameRequiredDescriptionDefault
periodsNo
series_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations exist, so the description carries full burden. It discloses the key behavioral trait: selecting comparison by calendar date rather than list position, which prevents off-by-one errors with missing values. This is a valuable behavioral detail.

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?

The description is concise: three sentences and a list of examples. Every sentence adds value, and the examples are clearly formatted. No wasted words.

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 tool's complexity (growth rate calculation with date-based comparison) and the existence of an output schema, the description covers the main behavioral aspects and usage. Could be more explicit about the output format, but the output schema handles that.

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 0%, so description must add meaning. It does not describe parameters individually, but the examples clarify that 'periods' controls the lag (e.g., periods=12 for year-over-year). This partially compensates for lack of schema descriptions.

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 clearly states it calculates absolute and percentage change for a FRED series, with examples showing usage. It distinguishes from sibling tools like 'compare_series' and 'get_series_observations' by focusing on growth rates.

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?

The description explains the date-based comparison method to avoid off-by-one errors, and provides examples for common use cases (year-over-year, quarter-over-quarter). It does not explicitly state when not to use, but the examples imply appropriate contexts.

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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