Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

fred_quick_indicator

Read-onlyIdempotent

Quick-access wrapper for the most-queried FRED indicators by friendly name. Avoids needing to memorize FRED series IDs. Valid indicators: unemployment_rate, fed_funds, fed_funds_target, cpi, core_cpi, gdp, real_gdp, ten_year_yield, two_year_yield, thirty_year_yield, thirty_year_mortgage, m2, industrial_production, retail_sales, nonfarm_payrolls, housing_starts, case_shiller, vix, wti, brent, natural_gas_henry_hub, dollar_index, consumer_sentiment, initial_claims, pce_inflation, recession_indicator.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoInclusive upper-bound ISO date (YYYY-MM-DD).
limitNoMaximum rows to return (default 50 for observations, 25 for catalog queries).
startNoInclusive lower-bound ISO date (YYYY-MM-DD).
indicatorYesFriendly indicator name. See description for valid options.

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that this is a convenience wrapper mapping friendly names to FRED series IDs and that only the listed indicators are valid, but it does not disclose default behavior, rate limits, or return characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded and the wording is economical. The long valid-indicators list is useful but redundant with the schema enum, adding some bloat; still, no filler sentences or vague marketing language appear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers what the tool does and enumerates valid inputs, and the schema covers all parameters. However, with no output schema, the description does not explain return types, units, frequency, or whether this returns a full observation series, leaving an agent with partial expectations.

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 coverage is 100%, so start, end, limit, and indicator are already described. The description mostly duplicates the indicator enum list without adding extra meaning for date handling, default limits, or output behavior, so it stays at the baseline.

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 states a specific resource and scope: a 'quick-access wrapper' for common FRED indicators using friendly names. It clearly distinguishes itself from series-ID-based FRED tools by saying it 'avoids needing to memorize FRED series IDs', though it never explicitly names an alternative.

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?

The description implies when to use this tool: when you know a friendly indicator name and want quick access to commonly queried FRED data. It contrasts with tools requiring raw FRED series IDs, but it does not explicitly mention sibling tools or state when NOT to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LiveDataLink toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources