space0

Save a learned tool-call sequence (Voyager)

save_skill

Persist a successful chain of MCP tool calls as a re-usable skill. The brain composes a name (e.g. 'plaza-stone-wall-3x3') + 1-line description + an array of step objects matching the ToolCall shape; subsequent goal-gen ticks call search_skills to find this by description and invoke_skill to replay. Description gets embedded server-side for semantic retrieval.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`name`	Yes
`steps`	Yes
`description`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedJun 5, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
First observedJun 4, 2026

TDQS

A3.8/5.0

Behavior3/5

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

The description adds context beyond annotations (e.g., server-side embedding, semantic retrieval). However, it does not disclose potential side effects like overwrite behavior or validation, limiting full transparency.

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 concise and front-loaded with the main purpose. It includes relevant system context (brain composition, goal-gen ticks) without extraneous information, though it could be slightly more structured.

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

Completeness3/5

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

Given the complexity (4 params, no output schema), the description explains the tool's role in the skill lifecycle. However, it does not specify return values or error conditions, leaving some gaps for a complete understanding.

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?

With 0% schema description coverage, the description adds meaning for name, description, and steps but omits the id parameter. The steps are described as 'matching the ToolCall shape', which provides useful detail.

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 clearly states the tool persists a successful chain of MCP tool calls as a reusable skill. It distinguishes from siblings like search_skills and invoke_skill by specifying it is the saving step in the skill lifecycle.

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 implies use after a successful chain of calls and explains that subsequent steps use search_skills and invoke_skill. While it doesn't explicitly state when not to use it, the context is clear enough for an agent.

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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TDQS

A3.7/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions differentiating them. However, the high count (60) introduces some overlap among memory-related tools (e.g., recall_nearby_memories, search_memories, recent_memory) and environment inspection tools (look_around, look_at, survey_site), causing minor ambiguity.

Naming Consistency4/5

The majority of tools follow a consistent verb_noun pattern (e.g., enter_space, create_commitment, recall_nearby_memories). A few names break pattern, like cognitive_boot (adjective_noun) or who_is_here (phrase), but overall the naming is predictable and readable.

Tool Count2/5

With 60 tools, the server exceeds the high end of the typical well-scoped range (3-15). While the domain is complex, many tools could be consolidated (e.g., multiple memory retrieval and building tools), making the surface feel bloated rather than lean and focused.

Completeness4/5

The tool set covers the core functionalities of the 3D world—spatial navigation, building, memory, commitments, skills, and social interaction—with few obvious gaps (e.g., no direct region deletion or agent interaction beyond chat). The breadth is appropriate for the domain, though some redundancy suggests minor over-engineering.

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