Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

fred_observations

Read-onlyIdempotent

Get time-series observations for a FRED series ID. Workhorse query for any economic indicator. Optional date range, units transformation (lin, chg, pch, log, etc.), and frequency aggregation (m, q, a).

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).
unitsNoUnits transformation: 'lin' (default), 'chg' (change), 'ch1' (change YoY), 'pch' (% change), 'pc1' (% change YoY), 'log', etc.
frequencyNoAggregate to a different frequency: 'd', 'w', 'bw', 'm', 'q', 'sa', 'a'.
series_idYesFRED series ID (e.g. 'GDP', 'UNRATE'). See https://fred.stlouisfed.org/ for the catalog.
aggregation_methodNoAggregation method when changing frequency: 'avg', 'sum', or 'eop' (end of period).

Schema Changelog

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

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds optional date range, units transformation, and frequency aggregation, but does not disclose pagination, default limits, or error behavior beyond what the schema already lists.

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?

Two sentences with no filler; the core purpose is front-loaded and the optional parameters are summarized compactly. Every sentence earns its place.

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

Completeness4/5

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

For a simple read-only observations query, the description plus fully documented schema and safety annotations are nearly complete. The only minor gap is the lack of explicit comparison to sibling FRED tools, but the tool call itself is fully specified.

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 description coverage is 100%, so the baseline is 3. The description paraphrases units and frequency options with examples such as 'lin, chg, pch, log' and 'm, q, a', but adds no meaning beyond the schema's own parameter descriptions.

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?

States a specific verb and resource: 'Get time-series observations for a FRED series ID.' This is clear and distinguishable from fred_search and fred_series_info, but it does not explicitly differentiate from fred_quick_indicator or fred_compare, which also retrieve FRED indicator data.

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

Usage Guidelines3/5

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

The phrase 'Workhorse query for any economic indicator' implies this is the default tool for FRED data retrieval, offering clear context. However, it gives no explicit when-not-to-use guidance or alternatives, leaving the agent to infer boundaries versus similar sibling tools.

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

B3.3/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources