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Glama

Fred

by io.github.pipeworx-io

Fred Search

fred_search
Read-onlyIdempotent

Search for economic data series by keyword. Returns series IDs, titles, and descriptions to identify the right indicator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-1000, default 20)
_apiKeyYesFRED API key
order_byNoOrder results by: search_rank, series_id, title, units, frequency, seasonal_adjustment, realtime_start, realtime_end, last_updated, observation_start, observation_end, popularity, group_popularity. Default: search_rank
sort_orderNoSort direction: asc or desc. Default: asc for search_rank
search_textYesKeywords to search for (e.g., "mortgage rate", "housing starts")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoPresent when the result list was truncated.
seriesYesList of matching series
returnedNoHow many series are in `series` (capped by `limit`, default 20).
truncatedNoTrue when `returned` is less than `total_matches`.
total_matchesYesTotal number of matching series

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • addedOutput schema / properties / note
      Added value: +{
+  "description": "Present when the result list was truncated.",
+  "type": "string"
+}
    • addedOutput schema / properties / returned
      Added value: +{
+  "description": "How many series are in `series` (capped by `limit`, default 20).",
+  "type": "number"
+}
    • addedOutput schema / properties / truncated
      Added value: +{
+  "description": "True when `returned` is less than `total_matches`.",
+  "type": "boolean"
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "series": {
+      "description": "List of matching series",
+      "items": {
+        "properties": {
+          "frequency": {
+            "description": "Data frequency (d, w, m, q, a, etc.)",
+            "type": "string"
+          },
+          "notes": {
+            "description": "Additional notes about the series",
+            "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 method",
+            "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"
+          }
+        },
+        "required": [
+          "series_id",
+          "title",
+          "units",
+          "frequency",
+          "seasonal_adjustment",
+          "observation_start",
+          "observation_end",
+          "popularity",
+          "notes"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_matches": {
+      "description": "Total number of matching series",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total_matches",
+    "series"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-fred-api-key",
+    "limit": 10,
+    "search_text": "mortgage rate"
+  },
+  {
+    "_apiKey": "your-fred-api-key",
+    "order_by": "popularity",
+    "search_text": "unemployment",
+    "sort_order": "desc"
+  }
+]
  4. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds that results include IDs, titles, and descriptions, which explains the shape of what the agent will receive. No contradiction and no missing red flags for a read-only search.

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?

Single, well-ordered sentence with no filler; the core action comes first and the return value is stated second.

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 search tool with output schema and read-only annotations, the essential context is present: what is searched and what comes back. It does not mention that fred_get_series would be the next step to fetch actual data, but that is a usage guidance gap rather than a completeness failure. Minor gap: no mention of metadata-only scope.

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 covers all 5 parameters with descriptions, so the tool description adds no extra parameter meaning. Baseline 3 applies because 100% coverage means the schema does the heavy lifting.

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?

States a specific verb ('Search') and resource ('economic data series') and describes the return payload ('series IDs, titles, and descriptions'), clearly positioning it against siblings like fred_get_series as the discovery entry point.

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?

The description implies a discovery use case but does not explicitly state when to prefer this over fred_get_series, fred_category, or fred_releases, nor when to switch. It names no alternatives or 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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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