Use when valuing an acquisition target, benchmarking deal pricing, or preparing a fairness opinion. M&A transaction multiples — acquisition EV/EBITDA, EV/Revenue, and control premiums by industry and deal size. Source: Damodaran transaction dataset and public deal aggregates. Used by corp dev, PE deal teams, M&A advisors, and CFOs preparing fairness opinions.
| Name | Required | Description | Default |
|---|---|---|---|
| industry | Yes | ||
| deal_size_tier | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds the data source (Damodaran dataset and public deal aggregates) and the metrics available, but does not disclose additional behavioral traits such as update frequency, data limitations, or output format. No contradiction with annotations.
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 sentences, front-loaded with the use case, followed by the data provided and source/audience. All sentences are relevant, though the audience sentence is somewhat redundant with the first sentence. No waste, but not as tight as a two-sentence version.
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 two-parameter tool with no output schema, the description covers purpose, use cases, metrics, dimensions, source, and audience. It does not describe the exact response shape, but the simple nature of the tool and the provided context make it adequately complete.
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 coverage is 0%, but the description explicitly mentions 'by industry and deal size,' directly mapping to the two parameters (industry, deal_size_tier). It also describes the return metrics (EV/EBITDA, EV/Revenue, control premiums), adding meaningful context beyond the schema's enum values.
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 M&A transaction multiples (acquisition EV/EBITDA, EV/Revenue, control premiums) for valuing acquisition targets, benchmarking deal pricing, and fairness opinions. It distinguishes from siblings like get_public_market_multiples by emphasizing acquisition-specific metrics and sources.
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 valuing an acquisition target, benchmarking deal pricing, or preparing a fairness opinion,' giving clear contexts. It does not explicitly name alternatives or when not to use it, but the use cases are sufficiently specific to guide selection.
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.