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Fred

by io.github.pipeworx-io
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Fred Series Info

fred_series_info
Read-onlyIdempotent

Get metadata for a series: title, units, frequency, seasonal adjustment, notes, and date range. Check this before fetching historical data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_apiKeyYesFRED API key
series_idYesFRED series ID (e.g., "MORTGAGE30US")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesYesAdditional notes and methodology
titleYesFull title of the series
unitsYesUnits of measurement
frequencyYesData frequency description
series_idYesFRED series ID
popularityYesSeries popularity score
units_shortYesAbbreviated units
last_updatedYesLast update timestamp
frequency_shortYesAbbreviated frequency code
observation_endYesLatest available observation date
observation_startYesFirst available observation date
seasonal_adjustmentYesSeasonal adjustment description
seasonal_adjustment_shortYesAbbreviated seasonal adjustment code

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "_apiKey": "your-fred-api-key",
-    "series_id": "MORTGAGE30US"
-  }
-]New value: +[
+  {
+    "series_id": "MORTGAGE30US"
+  }
+]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "frequency": {
+      "description": "Data frequency description",
+      "type": "string"
+    },
+    "frequency_short": {
+      "description": "Abbreviated frequency code",
+      "type": "string"
+    },
+    "last_updated": {
+      "description": "Last update timestamp",
+      "type": "string"
+    },
+    "notes": {
+      "description": "Additional notes and methodology",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "observation_end": {
+      "description": "Latest available observation date",
+      "type": "string"
+    },
+    "observation_start": {
+      "description": "First available observation date",
+      "type": "string"
+    },
+    "popularity": {
+      "description": "Series popularity score",
+      "type": "number"
+    },
+    "seasonal_adjustment": {
+      "description": "Seasonal adjustment description",
+      "type": "string"
+    },
+    "seasonal_adjustment_short": {
+      "description": "Abbreviated seasonal adjustment code",
+      "type": "string"
+    },
+    "series_id": {
+      "description": "FRED series ID",
+      "type": "string"
+    },
+    "title": {
+      "description": "Full title of the series",
+      "type": "string"
+    },
+    "units": {
+      "description": "Units of measurement",
+      "type": "string"
+    },
+    "units_short": {
+      "description": "Abbreviated units",
+      "type": "string"
+    }
+  },
+  "required": [
+    "series_id",
+    "title",
+    "units",
+    "units_short",
+    "frequency",
+    "frequency_short",
+    "seasonal_adjustment",
+    "seasonal_adjustment_short",
+    "observation_start",
+    "observation_end",
+    "last_updated",
+    "popularity",
+    "notes"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-fred-api-key",
+    "series_id": "MORTGAGE30US"
+  }
+]
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by clarifying that this is a metadata-only lookup and by framing it as a precursor to fetching historical data, which is useful behavioral context beyond the annotations.

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 two short sentences with no filler. The metadata content list is front-loaded, and the usage directive follows immediately. Every word earns its place.

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

Completeness5/5

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

This is a low-complexity tool with only two required parameters, both fully described in the schema, a rich output schema, and strong annotations. The description adds the missing workflow context ('check this before fetching historical data'), making it complete enough for an agent to invoke correctly.

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 100%, with both series_id and _apiKey documented in the schema. The description does not add parameter-specific guidance beyond calling it a series metadata lookup, so the baseline of 3 is appropriate since the schema carries the parameter burden.

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 uses a specific verb-resource pair ('Get metadata for a series') and enumerates the exact content returned: title, units, frequency, seasonal adjustment, notes, and date range. This clearly distinguishes it from fred_get_series, which presumably returns historical data.

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?

'Check this before fetching historical data' gives a clear usage context and tells the agent when to call this tool. It does not explicitly name an alternative or provide when-not-to-use conditions, but the intended workflow is obvious from the phrasing.

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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