Compare Latin American markets for expansion and calculate market-entry test economics.
Scored across 2 tools
The two tools have clearly distinct purposes: one performs arithmetic on test economics, while the other compares country readiness across operational dimensions. There is no meaningful overlap in inputs or outputs that would cause misselection.
Both names follow the same snake_case verb_noun-style pattern with parallel structure: calculate_latam_test_economics and compare_latam_market_readiness. The convention is predictable and readable.
Two tools is thin for a market-fit evaluation server, though each has a clear role. The surface feels borderline rather than clearly well-scoped, since there are only two analytical operations.
The tools cover financial viability and operational readiness comparison, which are core to LATAM market fit assessment. Some gaps remain, such as combining results across countries or supporting broader demand/market-sizing analysis, but these are minor for the stated narrow purpose.
Calculate contribution per sale and sales required to recover an entry-test budget, plus observed customer acquisition cost when both spend and new paying customers are supplied. All monetary inputs must use the same currency. This is arithmetic for a business experiment, not a revenue forecast or investment recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| currency | Yes | Single currency code for every monetary input; no exchange conversion is performed. | |
| test_budget | Yes | ||
| observed_spend | No | ||
| revenue_per_sale | Yes | ||
| observed_customers | No | ||
| variable_cost_per_sale | Yes |
| Name | Required | Description |
|---|---|---|
| currency | Yes | |
| decision | Yes | |
| formulas | Yes | |
| cac_status | Yes | |
| limitation | Yes | |
| observed_cac | Yes | |
| break_even_sales | Yes | |
| contribution_per_sale | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, closed-world behavior, so the description only needs to add computation semantics. It does add two useful traits beyond the annotations: no exchange conversion is performed, and observed CAC is only produced when both spend and customer counts are supplied — plus a clear interpretive limit on the results.
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?
Three sentences with the output definition and the currency constraint front-loaded, followed by the scope disclaimer. Dense and free of filler, though the opening sentence packs three separate outputs into one long clause.
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 a full output schema and rich annotations supplied, the description does not need to explain return values or safety. Its remaining gap is edge-case behavior — e.g. what happens when variable cost exceeds revenue per sale (zero or negative contribution) and therefore an unrecoverable budget — which matters for an arithmetic tool an agent will call with arbitrary inputs.
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 only 17%, so the description must carry most of the parameter burden. It compensates for the currency parameter (single-currency, no conversion) and explains the coupling of observed_spend and observed_customers, but says nothing about revenue_per_sale, variable_cost_per_sale, or test_budget beyond their self-descriptive names.
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 names a specific verb ('calculate') and enumerates the concrete outputs: contribution per sale, sales required to recover the test budget, and observed CAC. It is clearly distinct from a readiness-comparison tool, but it never names the sibling compare_latam_market_readiness, so the differentiation is implicit rather than stated.
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?
It gives a usable precondition ('all monetary inputs must use the same currency') and an explicit scope exclusion ('not a revenue forecast or investment recommendation'), which tells the agent when the output should and should not be relied upon. It does not, however, say when to prefer this over the sibling readiness tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare one to three Latin American countries using supplied operational statuses. Identify blockers, adaptations and missing evidence across seven dimensions. Missing dimensions remain unknown; results do not rank demand or verify the classifications. No personal data required.
| Name | Required | Description | Default |
|---|---|---|---|
| markets | Yes |
| Name | Required | Description |
|---|---|---|
| basis | Yes | |
| markets | Yes | |
| limitation | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive and closed-world behavior, so the bar is lower. The description still adds valuable disclosure beyond them: missing dimensions stay unknown, results do not validate classifications or rank demand, and no personal data is needed.
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?
Four compact sentences, front-loaded with the core action and followed by scope and limits; nothing is wasted. There is minor overlap between 'Missing dimensions remain unknown' and the same rule embedded in the schema's dimensions description.
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?
An output schema exists, so return values need not be explained. The description covers input preconditions, dimensional scope, and interpretive limits, which is sufficient for a single-parameter read-only comparison tool.
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?
Although reported schema coverage is 0%, the description adds meaningful semantics: a hard cap of one to three markets, the seven-dimension frame, and the rule that absent dimensions are treated as unknown rather than ready. It does not restate the country enum or status values, which the schema already carries.
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?
States a specific verb and resource ('Compare one to three Latin American countries') plus the scope of the analysis (seven dimensions, blockers/adaptations/missing evidence). It is clearly distinguishable from calculate_latam_test_economics, but never names the sibling explicitly.
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?
'Using supplied operational statuses' implies the caller must bring pre-researched facts rather than expecting the tool to discover them, and the exclusions ('do not rank demand or verify the classifications') set expectations. However, there is no explicit when-to-use-vs-sibling routing, so the agent must infer the boundary with calculate_latam_test_economics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
calculate_latam_test_economicscompare_latam_market_readinessGlobal expansion intelligence: country info, expansion cost estimates, competitor analysis.
Marketing and ecommerce data for LATAM brands: Meta, Google, TikTok, VTEX, Tiendanube, MercadoLibre
Growth analytics for LatAm ecommerce: MercadoLibre, Tiendanube, Shopify, Meta Ads and Google Ads.
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