Get metadata for a FRED economic data series by ID. Returns title, units, frequency, seasonal adjustment, observation range, and notes. Useful for verifying a series exists and understanding its measurement before pulling observations.
| Name | Required | Description | Default |
|---|---|---|---|
| series_id | Yes | FRED series ID (e.g. 'GDP', 'UNRATE', 'CPIAUCSL', 'DGS10'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds behavioral value by specifying exactly what metadata is returned and by framing the tool as an existence/measurement verification step before observations are pulled.
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 well-ordered: it states the core action, lists returned fields in a clean sequence, and ends with a practical use case. Every sentence contributes without redundancy or filler.
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?
With one required parameter, rich read-only annotations, and no output schema, the description covers the essential context: what is fetched, what fields come back, and why an agent would call it. It could mention invalid-ID behavior or a more formal return shape, but this is a simple metadata lookup and the description is largely complete.
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?
There is only one parameter, series_id, and the schema description covers it fully with examples ('GDP', 'UNRATE', etc.). The description only adds the phrase 'by ID', so it does not meaningfully extend the schema documentation. Baseline 3 is appropriate given 100% schema description coverage.
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 opens with a specific verb and resource: 'Get metadata for a FRED economic data series by ID.' It also enumerates the returned fields ('title, units, frequency, seasonal adjustment, observation range, and notes'), which clearly differentiates this metadata tool from observation-fetching siblings like fred_observations.
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 gives clear usage context: 'Useful for verifying a series exists and understanding its measurement before pulling observations.' This signals when to use it relative to data-retrieval tools, though it does not explicitly name or contrast sibling alternatives like fred_search or fred_observations.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.