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aimm_init_project

Initialize the AIMM data model by creating the folder skeleton (aimm.json, tables/, connections/, diagnostics.log). Requires a project name; accepts optional description and dialect.

Instructions

Bootstrap the AIMM data model at ~/Documents/AIMM/. Creates the folder skeleton (aimm.json + tables/ + connections/ + diagnostics.log) if it doesn't exist. Idempotent — safe to call when already initialised. Required argument: name, a human-readable label for the project that shows up in every context dump. Optional: description, free-text context the agent reads on every call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesProject name (max 120 chars). Required.
descriptionNoFree-text project context. Max 20,000 chars. Defaults to '' when omitted. Use this to capture the business domain the model covers — agents read it on every call.
dialectNoDefault SQL dialect for the project ('tsql', 'trino', 'spark'). Falls back to 'tsql' when omitted. Engines on individual connections override this for their own queries.
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses idempotency, folder creation, and parameter roles. It does not mention permissions or side effects, but for a bootstrap tool the information is sufficient.

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

Conciseness5/5

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

Two sentences front-load the core purpose and idempotency. Every sentence adds value with no redundancy. Extremely efficient.

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

Completeness4/5

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

Given the tool's simplicity (3 params, no output schema, single sibling), the description covers essential aspects: purpose, location, idempotency, and parameter explanations. Minor omission of return value but not critical for a bootstrap function.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the purpose of `name` (human-readable label for context dumps) and `description` (free-text read on every call), and notes dialect default. This enriches understanding beyond the schema.

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's action ('Bootstrap the AIMM data model'), the target location, and the folder skeleton created. It distinguishes itself from the sibling `aimm_read_project_context` by being an initialization tool versus a read tool.

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 explicitly states idempotency ('safe to call when already initialised'), which guides usage. However, it does not provide explicit when-not-to-use scenarios or mention alternatives beyond the implicit contrast with the sibling.

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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