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get_name_twin

Finds the names whose popularity trajectory over time is most similar to the given name (cosine similarity on the yearly series), i.e. names that rose and fell together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sexNoRestrict to this sex (defaults to the name's most common sex)
nameYesThe baby name to find trajectory twins for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the algorithm (cosine similarity) and interpretation ('rose and fell together'), which is useful. However, it does not describe the return format, whether the original name is included, limits, or error handling for unknown names, leaving some behavioral gaps.

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 a single sentence, front-loaded with the key action and outcome, and includes a helpful parenthetical clarification. Every word earns its place with no 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?

The tool is relatively simple with only two parameters and no output schema. The description explains the core functionality and algorithm, which is sufficient for an agent to invoke it, though a note about return values (e.g., list of names, perhaps with scores) would be even better.

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% since both 'name' and 'sex' have descriptions. The tool description adds context about the algorithm but does not add parameter-specific details beyond the schema. With full schema coverage, a baseline of 3 is appropriate.

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 uses a specific verb ('Finds the names') and clearly states the resource (names with similar popularity trajectories) and the method (cosine similarity on yearly series). This clearly distinguishes it from sibling tools like search_names or compare_names.

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 clearly implies when to use: when you have a name and want to find names that rose and fell together. It does not explicitly name alternatives or exclusions, but the context is unambiguous, so it's clear without being explicit.

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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