Geo-Parity

Select the lowest-latency cloud host region

select_region

Read-onlyIdempotent

Given a user-population breakdown, find the cloud host region that minimizes population-weighted round-trip latency (argmin_j Σ_i P_i × L_ij), and compute the optimal multi-region split (each user region routed to its nearest host). Latency is estimated from great-circle distance via a fibre-propagation model, not measured.

Input Schema

TableJSON Schema

Name	Required	Description
`hosts`	No	Optional comma-separated host region ids to restrict candidates (e.g. 'us-east-1,eu-west-2').
`users`	Yes	Comma-separated region:percent pairs (percentages auto-normalize), e.g. 'US-East:40,UK:30,Germany:30'. Region ids: US-East, US-West, Canada, Brazil, UK, Germany, France, Spain, Nigeria, South-Africa, UAE, India, Singapore, China, Japan, Australia.
`provider`	No	Optional cloud provider filter, applied before the hosts whitelist.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`best`	No
`split`	No
`ranking`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context beyond those: latency is estimated from great-circle distance via a fibre-propagation model rather than measured, and the optimal multi-region split is computed alongside the single best region. This helps set expectations about result reliability.

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 compact and front-loaded: it states the core purpose first, then the multi-region split, then the critical modeling caveat. The formula is dense but earns its place by specifying the exact optimization. No redundant sentences.

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

Completeness5/5

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

The description is complete for a read-only selection tool: it explains what it computes, how latency is estimated, and that multi-region routing is also handled. With an output schema present, return values need no elaboration, and annotations cover safety/idempotence.

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 description coverage is 100%, so the schema already documents all three parameters. The description reinforces that 'users' represents a user-population breakdown and explains the optimization objective, but it does not add significant new parameter-level meaning beyond what the schema provides.

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 states a precise verb-resource pair: 'find the cloud host region that minimizes population-weighted round-t-trip latency' and also 'compute the optimal multi-region split'. It clearly distinguishes the tool from siblings like compare_connectivity by focusing on latency minimization and region selection.

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 gives clear context: this is for selecting cloud host regions based on a user-population breakdown. It does not explicitly name alternatives or exclusion criteria, but the 'Given a user-population breakdown' framing makes the intended use case unambiguous.

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

Disambiguation3/5

Several tools occupy adjacent territory: check_travel_residency explicitly supersedes check_residency, check_schengen, and check_tax_residency_risk, and the cost-of-living family (arrival_cost, compare_total_cost, compare_housing, estimate_micro_costs, calculate_ppp, localize_salary) has overlapping price-level concepts. The detailed descriptions mostly clarify boundaries, but an agent selecting by name alone could easily pick the wrong residency or cost tool.

Naming Consistency4/5

Most tools follow a clean verb_noun snake_case pattern with recognizable prefixes (compare_*, check_*, find_*, calculate_*), which makes the set predictable. Deviations like arrival_cost, passport_power, required_rate, and the acronym-heavy calculate_ppp keep it from being perfectly uniform.

Tool Count3/5

26 tools is a heavy surface, and at least three of the check_* tools are explicitly superseded by check_travel_residency, adding redundant weight. The broad geo-arbitrage/nomad domain justifies much of the breadth, but the set feels closer to a full toolkit than a focused server.

Completeness4/5

The toolset covers the core remote-work and geo-arbitrage decision space: cost of living, purchasing power, tax residency risk, nomad visas, housing, healthcare, climate, connectivity, holidays, salary localization, FIRE, livability, and even team timezone planning. Gaps are minor—no family-size cost tool, no immigration/citizenship pathway data, and no dedicated safety/crime dataset beyond the livability composite—but agents can work around them.

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