add_nla_strip

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

With no annotations, the description carries the full load and does a good job: it discloses that track_name is auto-created when missing, that an empty track_name adds a new track on top, and it gives semantic detail for blend modes, extrapolation, and influence. It does not cover failure modes (nonexistent action/object, non-armature targets) or whether existing strips are disturbed, which keeps it below 5 for a mutation tool.

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

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

The summary sentence and use cases are front-loaded before the Args block, and each arg line carries non-redundant information. The Google-style docstring is slightly verbose in formatting but has no filler sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter mutation tool with no annotations, the description covers placement, track creation, blending, timing, and return shape (a dict with track, strip, and frame range), and an output schema also exists. What is missing is preconditions/error behavior, which an agent would need before calling against unfamiliar scenes.

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

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

Schema description coverage is 0%, so the description must compensate entirely, and it does: all 11 parameters are documented with meaning, valid ranges (repeat 0.1–1000, scale 0.01–100, influence 0.0–1.0, blend_in/out 0+), enum semantics, and the notable '2 is half speed' clarification for scale. This is materially more than the bare titles in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a precise verb+resource ('Place an action on the NLA timeline as a strip') and immediately names three concrete use cases (repeat a walk cycle, sequence clips, layer a wave on a walk). This clearly separates it from the NLA siblings list_nla, list_strips, and remove_strip without needing to open any schema.

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

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

The three motivating examples tell the agent exactly what scenarios this tool is for, and the blend_type text steers toward COMBINE 'best for layering'. It stops short of naming alternatives (e.g. insert_keyframe, create_animation_path) or stating when-not to use it, so it is clear context rather than explicit routing.

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