Use when a macro agent needs a full live signal stack in one call. Returns Fed funds, 2s10s, VIX, BTC, WTI, silver, gold, DXY, SOFR, MOVE, verifiable FOMC facts, model interpretation, and cross-asset sentiment. Example: live rates, vol, and commodities with FOMC facts separated from forward-looking interpretation. Source: FRED/EIA.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the read-only, non-destructive nature. The description adds meaningful context: the data source (FRED/EIA), the distinction between 'verifiable FOMC facts' and 'model interpretation,' and the inclusion of cross-asset sentiment. These go beyond the annotation safety profile and help set expectations.
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?
Two sentences, front-loaded with the trigger condition and purpose, followed by a dense but complete list of included signals and a clarifying example. Every word serves a purpose; no filler or redundancy.
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?
For a zero-parameter tool with no output schema, the description thoroughly covers the response contents, data source, and a concrete use case. Nothing critical is omitted, and the tool's role in a macro context is well understood.
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?
With zero parameters and 100% schema coverage, the schema already conveys all needed parameter info. The description adds no parameter details, but none are required; the baseline of 4 applies.
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 states the tool's purpose directly: 'full live signal stack in one call' and enumerates the exact indicators returned (Fed funds, 2s10s, VIX, BTC, WTI, etc.). This clearly differentiates it from sibling tools that focus on single benchmarks or indicators.
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?
Opens with an explicit usage context: 'Use when a macro agent needs a full live signal stack in one call.' It implies this is a composite alternative to calling multiple individual data tools, but does not explicitly name alternatives or exclusions, so it stops short of a 5.
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.
Multiple tools have overlapping purposes, e.g., get_inflation_benchmark and get_bls_inflation_components, get_commodity_benchmark and get_agricultural_commodity_benchmark. Descriptions provide some differentiation, but many benchmark tools cover similar domains, leading to high potential for misselection.
All tools follow a consistent 'get_' prefix with snake_case nouns, e.g., get_inflation_benchmark, get_ma_multiples_benchmark. No mixing of conventions or irregular naming patterns.
46 tools is excessive for a server focused on financial benchmarks and intelligence. While the domain is broad, many tools could be consolidated. The high count may overwhelm agents and suggests insufficient scoping.
The toolset covers a wide range of financial data—benchmarks, regulatory filings, commodity prices—but lacks granular tools like individual stock prices or sector-specific indices. Some areas (e.g., credit unions) are well-covered, but other common financial operations (e.g., portfolio analytics) are absent.