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Knowledge packs that travel and return exactly

singularis_knowledge_packs
Idempotent

Send a working knowledge pack to another machine, run its operations on a node, and return exact state home. Supports create, drop, wake (2-of-3 shares), work, and return.

Instructions

Send a working knowledge pack to another machine and bring its results home exactly. Operations: create (from a tower file), drop (install a pack on a node), wake (open it with 2-of-3 shares; one share alone is refused), work (run ticks on the node), return (bring the state home; the homecoming is exact, difference zero). Inputs: op and the paths the op needs. Cost: one job per operation.

Input Schema

NameRequiredDescriptionDefault
opYes
paramsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.11

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare mutation, open-world behavior, idempotency, and non-destructiveness. The description adds useful behavioral context beyond that: wake requires 2-of-3 shares and refuses a single share, return is exact with zero difference, and each operation costs one job. It still does not describe failure modes or side effects of drop/create in detail.

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 compact and front-loads the tool's purpose before listing operations, inputs, and cost. The phrasing is occasionally abstract, but every sentence contributes useful information. It avoids unnecessary repetition.

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?

The tool is multi-operation and complex, and the description covers the operation set, share threshold, exact return behavior, and job cost. Because an output schema exists, return values need not be explained. However, the per-operation parameter requirements remain incomplete, which is a meaningful gap for invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must carry the burden of explaining parameters. It lists the valid op values in prose and says params are the paths an operation needs, but it does not map those paths to each operation or give names, types, or formats. This is only a partial compensation for an otherwise bare schema.

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

Purpose4/5

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

The description states a specific verb and resource: sending a working knowledge pack to another machine and bringing results home. It clearly enumerates the operations create, drop, wake, work, and return. It does not explicitly differentiate itself from any sibling tool, which keeps it from a 5.

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

Usage Guidelines3/5

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

The operation list gives implied guidance about what each operation does, including the 2-of-3 share requirement for wake. However, it does not say when to choose this tool over sibling tools or when not to use it. The guidance is operation-level rather than tool-selection-level.

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