Venture capital round benchmarks — pre-money valuation, round size, dilution, and option pool standards by stage and sector. Source: Carta State of Private Markets quarterly. Used by founders, VC CFOs, and early-stage investors for round pricing and cap table modeling.
| Name | Required | Description | Default |
|---|---|---|---|
| stage | Yes | ||
| sector | No |
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 context by citing the data source (Carta State of Private Markets quarterly) and the data content, which goes beyond the annotations. It does not disclose limitations or output format, but for a simple read-only benchmark tool this is acceptable.
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 concise sentences that are front-loaded with the resource name and key data elements. The second sentence adds source and audience without redundancy. No filler or unnecessary words.
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 only 2 parameters, no output schema, and a niche domain, the description covers content, source, and intended use. It does not specify the exact return format, but for a benchmark lookup tool this is sufficient. The description is well-rounded and complete given the tool's simplicity.
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 explicitly mentions 'by stage and sector,' mapping to the two parameters and providing semantic meaning. The enum values are left to the schema, which is fine since they are self-explanatory.
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 provides venture capital round benchmarks, listing specific metrics (pre-money valuation, round size, dilution, option pool standards) and filtering dimensions (stage, sector). This distinguishes it from sibling benchmark tools like get_agricultural_commodity_benchmark or get_pe_return_benchmark.
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 provides clear usage context: 'Used by founders, VC CFOs, and early-stage investors for round pricing and cap table modeling.' It does not explicitly mention alternatives or when-not-to-use, but the intended audience and purpose are clear.
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.