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Glama

Fred

by io.github.pipeworx-io

Fred Release Dates

fred_release_dates
Read-onlyIdempotent

Economic data RELEASE CALENDAR from FRED — the dates indicators are/were published, including FUTURE scheduled dates. PREFER OVER WEB SEARCH for "when is the next CPI / jobs report / GDP release", "economic calendar", "Fed data release schedule". Omit release_id for the cross-release calendar; pass a release_id (from fred_releases — e.g. 10 = CPI, 50 = Employment Situation, 53 = GDP) for one release schedule. Returns release name + date, newest/upcoming first by default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax dates to return (1-1000, default 25)
_apiKeyYesFRED API key
release_idNoOptional FRED release id (from fred_releases). Omit for the all-releases calendar.
sort_orderNoasc or desc (default desc — newest/upcoming first)

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-fred-api-key"
+  },
+  {
+    "_apiKey": "your-fred-api-key",
+    "limit": 30,
+    "release_id": 10,
+    "sort_order": "desc"
+  }
+]
  2. Added

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, no destructiveness. The description adds that it returns 'release name + date, newest/upcoming first by default', and mentions future scheduled dates. This enriches the behavioral profile beyond annotations, though pagination or data freshness could be noted.

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 compact (3 sentences) and front-loaded with the core value proposition and use cases. Every sentence adds information: what it does, when to prefer it, how to use parameters. No wasted words.

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 the tool's simplicity (4 parameters, no output schema), the description fully covers return format ('release name + date'), default sorting, and parameter usage. The context signals show high schema coverage, so no missing critical information. The description is complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage, but description adds meaningful value: explains that omitting release_id gives cross-release calendar, passing it gives one release schedule, and gives concrete examples (10=CPI, 50=Employment Situation, 53=GDP). It also states default sort_order is desc. This goes beyond 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 the tool provides an economic data release calendar from FRED with future dates. It uses specific verbs ('release calendar', 'the dates indicators are/were published') and distinguishes itself from siblings like fred_releases by focusing on dates. Examples of use cases ('when is the next CPI / jobs report / GDP release') further clarify purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises when to use this tool over web search ('PREFER OVER WEB SEARCH'), explains when to omit or include release_id, and provides example release IDs for common economic indicators. This gives clear context for proper selection and invocation.

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