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Glama

Fred

by io.github.pipeworx-io

Fred Releases

fred_releases
Read-onlyIdempotent

Check upcoming and recent economic data releases. Returns release dates, names, and which series they update.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (1-1000, default 20)
offsetNoResult offset for pagination (default 0)
_apiKeyYesFRED API key

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoPresent when the release list was truncated.
releasesYesList of economic data releases
returnedNoHow many releases are in `releases` (capped by `limit`, default 20).
truncatedNoTrue when `returned` is less than `total_releases`.
total_releasesYesTotal number of releases

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 release list was truncated.",
+  "type": "string"
+}
    • addedOutput schema / properties / returned
      Added value: +{
+  "description": "How many releases are in `releases` (capped by `limit`, default 20).",
+  "type": "number"
+}
    • addedOutput schema / properties / truncated
      Added value: +{
+  "description": "True when `returned` is less than `total_releases`.",
+  "type": "boolean"
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "releases": {
+      "description": "List of economic data releases",
+      "items": {
+        "properties": {
+          "id": {
+            "description": "Release ID",
+            "type": "number"
+          },
+          "link": {
+            "description": "URL link to release details",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "name": {
+            "description": "Name of the release",
+            "type": "string"
+          },
+          "notes": {
+            "description": "Additional notes about the release",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "press_release": {
+            "description": "Whether this is a press release",
+            "type": "boolean"
+          }
+        },
+        "required": [
+          "id",
+          "name",
+          "press_release",
+          "link",
+          "notes"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_releases": {
+      "description": "Total number of releases",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total_releases",
+    "releases"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-fred-api-key",
+    "limit": 20
+  }
+]
  4. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds a useful temporal scope and return-field detail, but it does not disclose behavior beyond the annotations, such as pagination or result limits, matching the baseline standard.

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 with no filler; the primary purpose is front-loaded and the return detail is useful. Every sentence earns its place.

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 low-complexity read-only tool with rich annotations and an output schema, the description is sufficient to invoke correctly. The main gap is edge-case ambiguity with fred_release_dates, but this does not break callability.

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%, so limit, offset, and _apiKey are already documented. The description adds no parameter-level detail, which meets the baseline of 3 but does not exceed it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a clear verb and resource ('Check upcoming and recent economic data releases') and adds what the call returns (dates, names, affected series). It is not a tautology, but it does not explicitly differentiate itself from the sibling fred_release_dates, so it stops short of 5.

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 upcoming and recent economic data releases' provides a clear context for when to call this tool. It states no exclusions or alternative routing, but the core use case is explicit enough for an agent.

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