Use when building a public comps table, benchmarking a private company valuation, or preparing a fundraising benchmark. Public market valuation multiples — EV/EBITDA, EV/Revenue, P/E, and P/S by sector with p25/p50/p75 bands. Source: Damodaran January 2024 dataset. Used for board prep, M&A pricing, fundraising benchmarks, and DCF sanity checks. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| sector | Yes | ||
| context | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds context about the data source and vintage (Damodaran January 2024), the 'Free' cost, and the percentile band output. It does not go deeper into behavior like caching or data coverage limitations, so it adds some but not rich behavioral detail.
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 a single paragraph of four sentences, front-loaded with 'Use when...' and containing no fluff. Minor redundancy exists ('benchmark' appears three times, 'fundraising' twice), and 'Free' is a bit tacked on, but overall it is efficient.
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 read-only, simple two-parameter tool with no output schema, the description is quite complete: it covers use cases, data content, source, and cost. It lacks an explicit list of sector enum values, but the schema supplies that. The return format is not described, but there is no output schema to match, and the context of use cases provides enough guidance.
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' for the sector parameter, and the use cases (IPO, fundraising, board prep) map directly to the context enum values. Yet it doesn't explicitly name or describe the parameters, leaving some inference needed.
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's purpose: fetching public market valuation multiples (EV/EBITDA, EV/Revenue, P/E, P/S) by sector with p25/p50/p75 bands. It differentiates from sibling tools by naming the source (Damodaran January 2024) and specific metrics, making it unmistakable.
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 states when to use it: 'building a public comps table, benchmarking a private company valuation, or preparing a fundraising benchmark.' It also lists downstream use cases like board prep and M&A pricing. However, it does not mention exclusions or name alternative tools for 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.