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LiveDataLink

by io.github.blackboxfoundry

fred_compare

Read-onlyIdempotent

Compare 2 to 5 FRED series side-by-side over the same date range. Returns observations for each series. Useful for ratio analysis (e.g. compare 10Y vs 2Y yield) or cross-series correlation.

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).
series_idsYes2 to 5 FRED series IDs.

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The description adds behavior beyond the annotations: it compares series side-by-side over the same date range and returns observations for each series. With readOnlyHint and idempotentHint already covering safety, this extra detail about output shape is valuable.

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?

Three sentences with no filler: the first states the core action and scope, the second states the output, the third gives practical use cases. Everything 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?

The description, combined with the fully-covered schema and annotations, gives an agent enough to invoke the tool correctly: required series_ids, optional start/end/limit, output type, and use cases. Minor gaps like non-overlapping date-range behavior are not necessary for selection and invocation.

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 schema already documents all four parameters. The description does not add parameter-specific details, which is acceptable at baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Compare'), the resource ('FRED series'), and the scope (2 to 5 series, same date range). It also specifies what is returned ('observations for each series'), which distinguishes it from single-series FRED tools 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.

Usage Guidelines4/5

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

It provides clear use cases ('ratio analysis', 'cross-series correlation') that signal when to choose this tool. It does not explicitly name alternatives or exclusions, but the context is strong enough for an agent to select this over single-series or search-focused FRED tools.

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

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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