List the most popular FRED series in a category. Category IDs are numeric (e.g. 32991 = Interest Rates, 32263 = Money Stock, 9 = National Accounts). Use this to browse FRED structurally rather than via search.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum rows to return (default 50 for observations, 25 for catalog queries). | |
| category_id | Yes | FRED category ID. See https://fred.stlouisfed.org/categories/ for the hierarchy. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint true, idempotentHint true, destructiveHint false). The description adds context by saying results are the most popular series in a category, which implies ordering and selection behavior. However, it does not disclose pagination behavior, response shape, or rate limits; with no output schema, more detail would have been useful.
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?
The description is compact and each sentence earns its place: the first states the core purpose, the second gives parameter context, and the third provides usage direction. No filler or redundancy.
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?
For a simple two-parameter read-only listing tool, the description is mostly complete: purpose, category ID semantics, and structural browsing intent are all covered. It could be more complete by connecting to fred_observations for actual data retrieval or noting whether output is metadata-only, but the annotations and schema make the tool safely callable.
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?
The input schema covers both parameters well, so the baseline is 3. The description actively enriches category_id by providing concrete numeric examples and their meanings (32991 = Interest Rates, 32263 = Money Stock, 9 = National Accounts), which helps an agent select a valid category ID without further research.
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 action (list), the resource (FRED series), and the scoping dimension (category with numeric IDs). It distinguishes itself from search by saying 'browse FRED structurally rather than via search,' though it does not explicitly name sibling tools like fred_observations or fred_series_info.
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?
The description gives a clear use case: browse FRED by category instead of searching. The example category IDs help an agent know what input is expected. It does not spell out when not to use this tool versus related siblings such as fred_observations or fred_series_info, so it stops short of full 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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.