Skip to main content
Glama

get_living

Determine if a person is likely alive and estimate probable birth/death dates using genealogical heuristics. Returns a living verdict or date estimates for a given person record.

Instructions

Estimate whether (or until when) a person is alive. Read-only.

mode=status returns {"living": bool} from /living/; mode=dates returns probable birth/death date estimates plus an explanation from /living//dates. Tune the heuristic with the three optional integer parameters (upstream defaults: 20 / 110 / 20).

Args: handle: Person handle. mode: "status" (default) or "dates". average_generation_gap: Average years between generations (>= 1). max_age_probably_alive: Max age in years still considered alive (>= 1). max_sibling_age_difference: Max sibling age gap tolerated (>= 1). instance: Gramps Web base URL from get_instances; default = first.

Returns: dict: {"status", "url", "data": }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNostatus
handleYes
instanceNo
average_generation_gapNo
max_age_probably_aliveNo
max_sibling_age_differenceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are present, so the description carries the full behavioral burden. It discloses read-only semantics, the two endpoint behaviors, heuristic defaults, and the return dict, which is strong context; it omits only peripheral details like error/edge-case behavior.

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 front-loaded with the core purpose, then uses compact code-style lines for modes and a structured Args/Returns layout. Every sentence adds actionable information without redundancy.

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?

For a 6-parameter tool with no output schema and no annotations, the description covers purpose, modes, parameters, defaults, and return shape. It lacks only a concrete example or an explanation of the 'status' key, but the overall invocation context is essentially complete.

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

Parameters5/5

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

Schema description coverage is 0%, but the Args section documents every parameter with its meaning, allowed values for mode, constraints for the heuristic integers, and the source of instance. This fully compensates for the schema gap.

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 opening line states a specific verb and resource: 'Estimate whether (or until when) a person is alive.' The read-only note and the mode/endpoint explanation make the intended domain unmistakable and separate it from the management/search siblings.

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 explains when to choose 'status' versus 'dates' and tells the agent to source the instance from get_instances with a default of first. It does not explicitly name sibling exclusions, but the purpose and mode guidance are clear enough for correct selection.

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