Use when a trader or portfolio manager needs current regime label and tactical positioning. Returns active regime, verifiable FOMC facts, live market snapshot, model interpretation, concurrent playbooks, and key levels. Example: regime label with playbook actions and risk triggers.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context by enumerating the returned data items (active regime, verifiable FOMC facts, live market snapshot, etc.), which goes beyond the annotations. It doesn't describe rate limits or auth, but none are implied.
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?
The description is three short sentences: a usage trigger, a list of return contents, and an example. Every sentence adds value with no repetition or filler. The most important usage instruction is front-loaded.
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?
With zero parameters and no output schema, the description fully carries the burden of explaining what the agent will receive. It lists six distinct output components and gives an example, making the tool's behavior clear and complete for a read-only information retrieval tool.
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?
The tool has zero parameters, so the baseline is 4. The description mentions requesting regime label and tactical positioning, which aligns with what the tool returns, though there are no parameter details to document.
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 clearly states the tool's purpose: it provides a current regime label and tactical positioning for traders/PMs. It also lists specific return components (FOMC facts, market snapshot, model interpretation, playbooks, key levels), distinguishing it from sibling benchmark tools that focus on single metrics.
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?
The description explicitly says 'Use when a trader or portfolio manager needs current regime label and tactical positioning,' giving a clear triggering context. It does not mention when not to use it or name alternative tools, but the context is sufficient for a zero-parameter read-only tool.
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.