Geo-Parity

Runway multiplier from relocating (bootstrap buffer)

calculate_runway

Read-onlyIdempotent

For founders/freelancers stretching savings: computes how many extra months of runway relocating to a cheaper hub buys. Burn is split into fixed (location-independent — subscriptions, debt, insurance) and local; only the local slice scales by the target's cost-of-living ratio (city-level where a curated hub is given, else national, incl. Factbook-only countries). Returns the home baseline plus each hub's monthly burn, runway months, extra months, run-out date and the ×multiplier. Estimates from purchasing-power data, not financial advice.

Input Schema

TableJSON Schema

Name	Required	Description
`burn`	Yes	Total current monthly burn at home (in `currency`).
`home`	No	Home country alpha-2 code. Default US.
`fixed`	No	Location-independent slice of burn (0…burn) that won't fall when you move.
`savings`	Yes	Total savings (in `currency`).
`targets`	Yes	1–3 hubs, comma-separated: COUNTRY or COUNTRY:citySlug (e.g. TH:chiang-mai,AR:buenos-aires,GE).
`currency`	No	ISO code for the money inputs (e.g. GBP). Default USD.
`homeCity`	No	Optional curated home-city slug.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	No
`home`	No
`hubs`	No	Sorted by extra months; each has runwayMonths, extraMonths, multiplier, monthlyBurnUsd, runsOut, priceRatio and a coverage note when approximate.
`fixedFloorMonths`	No	Runway cap even at zero local cost (savings ÷ fixed burn).

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already establish readOnly and idempotent behavior. The description adds valuable behavioral context: the fixed/local burn split, city-level vs national cost-of-living scaling, and the list of returned fields. It also appropriately frames the output as estimates from purchasing-power data, not financial advice.

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, front-loaded with the user benefit, and each sentence contributes: audience, computation, method, output, and caveat. No filler or redundant restatement of the tool name or schema.

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?

For a tool with 7 parameters and an output schema, the description covers the calculation approach, inputs, output fields, and limitations. Combined with the 100% schema coverage and safe annotations, an agent has everything needed to invoke it correctly and interpret results.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining how burn is split into fixed and local slices and how only the local slice scales by cost of living, which directly clarifies burn, fixed, and targets. This is genuine semantic enrichment, not schema repetition.

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 immediately states the specific outcome: computing extra months of runway from relocating to a cheaper hub. It defines the audience, the method, and the returned metrics, making it clearly distinct from siblings like compare_total_cost or calculate_ppp.

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?

It gives clear context for when this tool fits: founders/freelancers stretching savings by relocating to a cheaper hub. It does not explicitly name alternatives or exclusion conditions, but the use case is specific enough to guide selection among the sibling tools.

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.

Resources

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