fred_categoryBrowse economic data by category (housing, employment, money/banking, etc.). Returns subcategories and related series IDs.
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | FRED API key | |
| category_id | No | Category ID to browse children of (default: 0 for root) |
| Name | Required | Description | Default |
|---|---|---|---|
| categories | Yes | Child categories | |
| parent_category_id | Yes | Parent category ID |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "categories": {
+ "description": "Child categories",
+ "items": {
+ "properties": {
+ "id": {
+ "description": "Category ID",
+ "type": "number"
+ },
+ "name": {
+ "description": "Category name",
+ "type": "string"
+ },
+ "parent_id": {
+ "description": "Parent category ID",
+ "type": "number"
+ }
+ },
+ "required": [
+ "id",
+ "name",
+ "parent_id"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "parent_category_id": {
+ "description": "Parent category ID",
+ "type": "number"
+ }
+ },
+ "required": [
+ "parent_category_id",
+ "categories"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "_apiKey": "your-fred-api-key",
+ "category_id": 0
+ },
+ {
+ "_apiKey": "your-fred-api-key",
+ "category_id": 113
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that it returns subcategories and series IDs, providing 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, efficient sentence that front-loads the key purpose. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the annotations and output schema, the description provides enough context for a browse tool. Could mention pagination, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds minimal extra meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (browse), resource (economic data by category), and output (subcategories and related series IDs). It distinguishes from sibling tools like 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It describes the context for using this tool (to browse by category) and implicitly suggests alternatives for specific series data. However, it lacks explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.