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Simba MCP Server

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by getsimba-ai
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Python
Hybrid

Save Model

save_model
Destructive

Save an unsaved API-created model into a project so it appears in default listings and Saved Models. Optionally specify a project ID; re-saving renames or refiles.

Instructions

Save a model into a project under a display name.

API-created models start unsaved and are invisible to list_models (without include_unsaved=true) — saving files them into a project so they appear in the default listing and the dashboard's Saved Models.

The same saved-models cap applies as in the dashboard: at the cap the API returns a 400 with error_type "saved_limit". Re-saving an already-saved model renames/refiles it without consuming a new slot.

Args: model_hash: Hash of the model to save. name: Display name to save under (non-empty). project_id: Optional target project ID; must be a project you own or one shared with a team you belong to. Discover ids with list_projects; create a folder with create_project. Defaults to your default project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
model_hashYes
project_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "save_modelDictOutput",
+  "type": "object"
+}
  2. First observedv0.3.2

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial behavior beyond the annotations: unsaved models are invisible to list_models without include_unsaved=true, the saved-model cap returns a 400 with error_type 'saved_limit', re-saving does not consume a new slot, and project ownership is required. This is exactly the kind of contextual detail an agent needs.

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 front-loaded with a one-sentence purpose, then a useful lifecycle explanation, then an Args section. It is slightly long but every section contributes necessary behavioral or parameter guidance, so it earns its length.

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?

The description covers the core action, the unsaved-to-saved transition, error behavior, parameter constraints, ownership rules, and defaults. With an output schema already present, no important operational detail is missing.

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 description coverage is 0%, so the description carries the full burden. It explains model_hash, name (non-empty), and project_id (optional, must be owned or team-shared, defaults to default project), and even suggests how to discover project ids. All three parameters receive meaningful semantics.

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 opens with a specific action and resource: 'Save a model into a project under a display name.' It then clarifies the unsaved vs. saved lifecycle and visibility in list_models, which makes the tool's role distinct from siblings like create_model or rename_model.

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?

It gives a clear usage context: API-created models start unsaved and need this call to become visible in default listings. It also explains project_id constraints and points to list_projects/create_project. However, it does not explicitly state when not to use it or name alternative tools like rename_model or unsave_model.

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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