Use when valuing a business, setting hurdle rates, or benchmarking discount rates for M&A analysis or capital allocation. WACC benchmarks by sector and market cap tier from Damodaran annual dataset — used for DCF valuation, M&A pricing, board approval, and capital allocation. The most cited public finance benchmark. Updated January annually.
| Name | Required | Description | Default |
|---|---|---|---|
| sector | Yes | ||
| market_cap_tier | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable context about data source ('Damodaran annual dataset') and update frequency ('Updated January annually'), which helps set expectations about data freshness and provenance.
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 concise and front-loaded with the most important usage guidance. It delivers purpose, use cases, source, and update cadence in three sentences without redundancy or fluff.
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 simple read-only benchmark lookup, the description covers usage, data source, and update frequency. It doesn't describe the return format or structure, but given the tool's simplicity and the absence of an output schema, this is not a critical gap. The description is sufficiently complete for an agent to understand when and how to use it.
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?
Schema description coverage is 0%, so the description must compensate. It mentions 'by sector and market cap tier,' which maps directly to the two parameters, but provides no additional meaning beyond the enum names. The enum values themselves are self-explanatory enough for basic use, but the description doesn't elaborate on how to choose or interpret them.
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 that the tool provides WACC benchmarks by sector and market cap tier from the Damodaran annual dataset. It uses a specific verb and resource, distinguishing it from sibling benchmark tools by explicitly naming WACC and its data source.
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 first sentence gives explicit use cases: 'Use when valuing a business, setting hurdle rates, or benchmarking discount rates for M&A analysis or capital allocation.' This provides clear context for when to invoke the tool, though it does not mention alternatives or when not to use it.
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.