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Glama

Fred

by io.github.pipeworx-io

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. Dates show when Glama detected each change.

  1. 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"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-fred-api-key",
+    "series_id": "MORTGAGE30US"
+  }
+]
  3. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, which fully cover safety. The description adds the list of returned metadata fields, which is useful context but not behavioral beyond what annotations provide. No contradiction.

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?

Two short sentences: first states purpose and output, second gives usage guidance. No wasted words, front-loaded with key information.

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?

Given low complexity, 2 params, and presence of output schema, the description is complete. It lists expected return fields and provides usage context. No gaps.

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% with clear descriptions for both parameters. The description does not add any extra meaning beyond the schema, so baseline score of 3 is appropriate.

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 the verb 'Get' and the resource 'metadata for a series', listing specific fields. It distinguishes from siblings by implying metadata vs data (contrast with fred_get_series).

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 phrase 'Check this before fetching historical data' provides clear usage context. While it does not explicitly name the sibling tool for data, the context strongly implies fred_get_series, and the instruction is direct.

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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TDQS

A4.3/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose with detailed descriptions that differentiate even closely related tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded. The FRED and Polymarket tool sets are well-organized with unique responsibilities. No two tools appear to do the same thing.

Naming Consistency4/5

Most tools follow a consistent verb_noun or noun_verb pattern in snake_case (e.g., resolve_entity, compare_entities, list_subscriptions). However, a few tools like 'forget', 'remember', and 'recall' deviate by being single verbs, and 'pipeworx_feedback' uses a noun_verb format. Overall, the naming is predictable but has minor inconsistencies.

Tool Count4/5

With 37 tools covering a broad domain (economic data, prediction markets, company profiles, subscriptions, memory, etc.), the count is reasonable and justifiable. It is slightly above the typical sweet spot but not excessive, and each tool serves a specific purpose. The scope is broad enough to warrant this many tools.

Completeness5/5

The server provides a comprehensive surface for its domain, including CRUD-like operations for data querying (ask_pipeworx, deep_research), specialized tools for prediction markets (arbitrage, edges), and utilities (memory, subscriptions). Obvious operations like entity resolution, comparison, and change tracking are present. No critical gaps are apparent for the stated purpose of querying structured data and engaging with prediction markets.

Resources