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LastLook Data

by io.github.zev-lll

Get Current FRED Series Value

get_current_value
Read-onlyIdempotent

Returns only the single most recent value for any supported FRED series. Cheaper than get_series ($0.01 vs $0.05). Use this when you need just the latest reading — e.g. current CPI, unemployment rate, mortgage rate. Use get_series instead when you need historical observations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
series_idYesFRED series ID e.g. CPIAUCSL, UNRATE, MORTGAGE30US, DGS10, DCOILWTICO, SAHMREALTIME

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesDate of the observation (YYYY-MM-DD)
labelYesHuman-readable series name
valueYesMost recent observed value
series_idYesFRED series identifier

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable behavioral context beyond annotations: it returns only the most recent value (not a time series) and is cheaper than get_series ($0.01 vs $0.05). This provides practical operational detail without contradicting annotations.

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?

The description is concise and front-loaded. The first sentence states the core function, and the remaining sentences provide cost, usage context, and alternative tool guidance. Every sentence earns its place with no fluff or redundancy.

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

Completeness5/5

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

Given the tool's simplicity (one parameter, output schema present) and strong annotations, the description fully covers the needed context: what it returns, when to use it, when not to, and the cost trade-off. The alternative tool is named, and the output schema obviates the need to describe return structure.

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%: the series_id parameter has a description and an enum list of valid FRED series IDs. The description does not add further semantic details about the parameter, only mentions 'any supported FRED series,' which is already implied by the schema. Since the schema carries the burden, the baseline of 3 is appropriate.

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 tool's function with a specific verb+resource: 'Returns only the single most recent value for any supported FRED series.' It explicitly distinguishes from siblings by mentioning 'single most recent value' and naming the alternative get_series for historical data.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Use this when you need just the latest reading — e.g. current CPI, unemployment rate, mortgage rate' and 'Use get_series instead when you need historical observations.' It also includes the cost advantage, making the when-to-use decision even clearer.

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

A3.9/5.0
Disambiguation3/5

The tool set has several overlapping functions, particularly among the bundle tools (e.g., get_bundle_context_brief vs get_bundle_macro both provide macro indicators, and get_bundle_rate_environment overlaps with get_yield_curve and get_policy_spread). Individual current/history/date tools are distinct but some redundancy exists (e.g., get_treasury_yield_current vs get_current_value for DGS30). Descriptions help differentiate purposes, but agents may still hesitate when selecting between similar bundles.

Naming Consistency4/5

All tool names begin with the verb 'get_' and use snake_case, creating a consistent pattern. The bundle tools are uniformly prefixed with 'get_bundle_', and individual data tools follow a get_[entity]_[modifier] structure (e.g., get_fx_rate_current, get_fx_rate_by_date, get_fx_rate_series). Minor deviations exist (e.g., get_series, get_current_value, get_value_by_date are less descriptive of the underlying entity), but overall the naming is predictable and readable.

Tool Count3/5

With 24 tools, the server is on the heavy side of the typical range. The broad domain (macro data, mortgage, crypto, FX, treasury, EDGAR) justifies many tools, but some could potentially be consolidated (e.g., individual rate tools vs rate bundles). The count does not feel overwhelming, but it is borderline heavy.

Completeness4/5

The server covers a wide range of economic and financial data with both bundled and granular views. It includes current, historical, and date-specific retrievals for FRED series, FX, and crypto, plus specialized tools for mortgages, recession, and yield curves. Minor gaps exist, such as no way to list all supported FRED series or full financial statements for EDGAR, but the core lifecycle of data querying is well covered.

Resources