Xearno Tools

Market Size (TAM · SAM · SOM)

market_size

Read-only

Bottom-up market sizing with a built-in plausibility check. Builds TAM, SAM, and SOM bottom-up from customer count and revenue per account, then sanity-checks whether the implied customer acquisition is actually plausible — the check most pitch decks skip.

Input Schema

TableJSON Schema

Name	Required	Description
`arpa`	No	Annual revenue per customer
`samPct`	No	Serviceable share (%) Share you can actually reach: your segment, geography, language, channel.
`somPct`	No	Obtainable share of SAM (%) Realistic share you win in ~3–5 years given competition.
`accounts`	No	Potential customers in market Total count of businesses/people who could conceivably buy this category.

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, so the description's extra detail about the plausibility check adds value without contradiction. It does not mention auth needs or side effects, but the read-only nature is clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. It front-loads the core purpose and adds a unique selling point (plausibility check) efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the key functionality and the unique plausibility check. However, without an output schema, it does not describe the return format or structure, which is a minor gap for a calculation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all parameters. The description adds a high-level summary ('customer count and revenue per account') but does not provide additional details beyond the schema, meeting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool does bottom-up market sizing (TAM, SAM, SOM) with a built-in plausibility check. It uses specific verbs like 'Builds' and 'sanity-checks', and is clearly distinct from sibling financial calculators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when performing market sizing, but does not explicitly state when not to use it or suggest alternatives. However, given the sibling tool list, the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0

Disambiguation4/5

Each tool targets a distinct niche (e.g., specific country tax rules, loan types, or legal calculations), with detailed descriptions that clarify boundaries. However, the large number of tools (66) could cause some confusion for an agent trying to select the right one for a general query, especially when multiple tools relate to the same country.

Naming Consistency4/5

Tool names follow a mostly predictable pattern: lowercase words separated by underscores, often starting with a country name (e.g., 'uk_stamp_duty_sdlt') or a topic (e.g., 'compound_growth'). There are minor deviations, such as abbreviations ('npv_irr', 'sip') and varying use of verbs, but overall the naming is clear and consistent.

Tool Count3/5

At 66 tools, the server is unusually large and covers an extensive range of financial and legal calculators. While each tool justifies its existence, the count exceeds the typical well-scoped range (3–15), making the server feel bloated. A more modular design might improve coherence.

Completeness4/5

The tool set covers a wide array of domains: personal income taxes, property taxes, loan calculations, investment returns, and specific country regulations. Minor gaps exist (e.g., missing tools for corporate taxes, general retirement planning, or insurance), but the overall coverage is thorough and addresses many niche scenarios that general AI handles poorly.

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