MPFB MCP

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mpfb_create_human

Destructive

Creates a new MakeHuman basemesh character from phenotype sliders and returns its name, the starting point for shaping, rigging, dressing, and skinning in Blender.

Instructions

Create a new MakeHuman character - a basemesh with a phenotype - through MPFB's HumanService.create_human(). The starting point for building a character: shape it further with mpfb_set_targets, rig it with mpfb_add_rig before dressing it with mpfb_add_asset, and give it a skin with mpfb_set_skin, without which it stays grey.

The eleven macro sliders (gender, age, muscle, weight, proportions, height, cupsize, firmness, race_asian, race_caucasian, race_african) are 0.0-1.0 and are the same sliders under the same names as mpfb_set_macro_details takes, so a character can be created and then adjusted without relearning the vocabulary. The two differ in what an omitted slider means: here every one has a default and the character is built from the full set, while there an omitted slider is left alone. Set the phenotype in this call when you know it; use mpfb_set_macro_details to change one afterwards.

0.5 is neutral for every slider except the three race weights, whose neutral is 0.33 each. age runs 0.0 = baby, 0.1875 = child, 0.5 = young adult, 1.0 = old; gender 0.0 = fully female, 1.0 = fully male. The three race weights are independent and MPFB does not normalize them, so the defaults sum to 0.99 and setting one to 1.0 does not reduce the other two.

scale is the basemesh scale factor (0.1 = decimeters, MPFB's own default). Change it only deliberately: mpfb_set_skin's enhanced skin types read it to pick subsurface radii.

create_human()'s own defaults are used for mask_helpers, detailed_helpers, extra_vertex_groups and feet_on_ground; this tool does not expose them.

On status: "ok", result carries basemesh_name - the address every later tool needs, since Blender uniquifies a name already in use - plus vertex_count, location and the macro_detail_dict that was applied, in MPFB's own nested shape. performed is true: creation either succeeds or raises, so this tool has no separate verb of its own.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`age`	No
`scale`	No
`gender`	No
`height`	No
`muscle`	No
`weight`	No
`cupsize`	No
`firmness`	No
`race_asian`	No
`proportions`	No
`race_african`	No
`race_caucasian`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 3, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations only supply destructiveHint=true, so the description carries most of the burden and delivers: it discloses the non-normalization of race weights, the effect of `scale` on later skin rendering, which create_human() defaults are silently used for unexposed parameters, and that creation 'either succeeds or raises'. It does not explicitly address the destructiveHint or scene-level side effects, but the disclosure is well beyond the annotations.

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?

Long, but front-loaded with purpose and workflow before parameter semantics and return values; each paragraph carries distinct information. Slightly verbose in places (e.g. the 'without relearning the vocabulary' aside), but nothing is padding.

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?

Despite an output schema existing, the description still explains the key result fields (basemesh_name as the address later tools need, vertex_count, location, macro_detail_dict) and the performed flag. Combined with full parameter semantics and workflow placement, an agent has everything needed to call it correctly.

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 coverage is 0%, and the description fully compensates: it lists all eleven macro sliders, their 0.0-1.0 range, per-slider neutral values (0.5 except 0.33 for the three race weights), the meaning of age and gender endpoints, and the semantics of `scale` as a basemesh scale factor.

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?

States a specific verb and resource ('Create a new MakeHuman character - a basemesh with a phenotype') and names the underlying service call. It differentiates itself from several siblings (mpfb_set_macro_details, mpfb_add_rig, mpfb_add_asset, mpfb_set_skin) and positions itself as the starting point of the character workflow.

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?

Gives explicit ordering guidance ('rig it before dressing it') and an explicit when-to-use-this-vs-alternative rule: 'Set the phenotype in this call when you know it; use mpfb_set_macro_details to change one afterwards.' It even spells out the semantic difference in omitted parameters between the two tools.

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