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set_voice_axes

Write the calibrated leans from a voice round into the FOUNDER'S OWN voice profile, the personal register, never the brand's. Two voices live here and they do not sound alike: the brand's came from the website and is what marketing pages and listings use, and this verb cannot touch it. WHEN: after a round, once an axis has enough answers to read. Send one write per round, not one per pair, and leave out an axis with too few observations: that is not a lean. Send only the axes that moved; the rest keep what they had. Free; requires the 'act' scope. WHAT COMES BACK: every axis on record, and the top of their board.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
axesYesKeyed by axis id, each { lean, strength }. lean is 'a' or 'b', the pole this voice sits nearer; strength is the share of that axis's observations that fell on the leaning side, so 0.5 is an even split and 1 is every observation one way.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

With only readOnlyHint=false and destructiveHint=false in the annotations, the description carries the behavioral burden, and it does so well: it discloses that this is a write operation, requires the 'act' scope, updates only the founder's profile, never the brand's, performs partial updates, and returns every axis on record plus the top of the board. This gives the agent a clear picture of side effects and response behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average, but the length is justified: it uses labeled sections (WHEN, WHAT COMES BACK), front-loads the core purpose, and each sentence adds operational context. Slight redundancy around 'this verb cannot touch it' is acceptable given the importance of preventing writes to the wrong profile.

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 single-parameter mutation tool with no output schema and sparse annotations, the description covers all essential context: target profile, non-target profile, timing, batching, partial updates, required scope, and the return payload. Nothing critical is missing for an agent to invoke the tool correctly.

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?

The input schema already describes the 'axes' parameter structure well, including the lean enum and strength range, so the baseline is 3. The description adds meaningful usage semantics by instructing to 'leave out an axis with too few observations' and 'send only the axes that moved', which directly affects how the parameter object should be populated.

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 specific action ('Write the calibrated leans...') and a precise target ('FOUNDER'S OWN voice profile, the personal register'), explicitly excluding the brand's profile. This distinguishes the tool from related voice/profile tools like get_voice_profile and clarifies exactly what resource it operates on.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit WHEN guidance ('after a round, once an axis has enough answers to read'), exclusions ('leave out an axis with too few observations'), and batching rules ('Send one write per round, not one per pair'). It also says to send only axes that moved, which helps the agent decide what to include and when to call the tool.

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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