Use when a technical trader needs wave counts, targets, and invalidation levels for major assets. Returns wave position, degree, target high/low, invalidation, and confidence for BTC, SPY, TLT, Gold. Example: wave label, target band, invalidation, and confidence score per asset.
| Name | Required | Description | Default |
|---|---|---|---|
| asset | No | Asset symbol or "all" (default all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context by enumerating the exact output fields (wave position, degree, target high/low, invalidation, confidence) and asset coverage (BTC, SPY, TLT, Gold), going beyond what annotations provide.
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?
Three sentences with front-loaded usage guidance, return summary, and a concrete example. Every sentence adds value without padding, making it both concise and logically ordered.
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?
Despite having no output schema, the description fully explains what data will be returned and for which assets. It also states the default behavior implicitly (all assets via 'all' parameter). The tool is simple, and the description covers all necessary context for an agent to select and invoke it correctly.
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 schema already fully describes the sole parameter 'asset' with 100% coverage. The description lists example assets, which mildly reinforces possible values, but does not add meaning beyond the schema's 'Asset symbol or all' note. Baseline 3 is appropriate.
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 identifies the tool as returning Elliott wave data (wave counts, targets, invalidation) for specific assets (BTC, SPY, TLT, Gold). It uses specific verbs ('Returns') and distinguishes itself from sibling benchmark tools by focusing on technical analysis for these named assets.
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 opening phrase 'Use when a technical trader needs...' provides explicit context for when to invoke this tool. It does not exclude alternatives or name sibling tools, but the use case is clearly defined and distinct from other data retrieval tools.
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.