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Simba MCP Server

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by getsimba-ai
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Python
Hybrid

List Models

list_models
Read-onlyIdempotent

List all marketing mix models for the authenticated user, showing status, type, and timestamps. Get the model hash needed for other model endpoints.

Instructions

List all Marketing Mix Models for the authenticated user.

Returns model name, hash, status (pending/under way/complete/failed), type (mmm/var), hierarchy value, and timestamps.

NOTE: All other model endpoints use model_hash (string, e.g. "f835671a25") as the identifier. Use the model_hash from this response.

Args: include_unsaved: Include draft/unsaved models (default false). limit: Maximum number of models to return (default 50, max 500). offset: Number of models to skip, for paging past limit (default 0).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
include_unsavedNo

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": "list_modelsDictOutput",
+  "type": "object"
+}
  2. First observedv0.3.2

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral detail beyond the annotations: it lists the returned fields, statuses, type values, hierarchy value, and timestamps, and explains the hash convention across related endpoints. This gives the agent a meaningful picture of what to expect without contradicting the annotations.

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?

The description is well structured and front-loaded: purpose first, then return values, then the critical identifier note, then parameter documentation. Every sentence adds value, and the Args block is concise and directly useful. No redundant fluff.

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?

For a read-only list endpoint with an output schema and comprehensive annotations, the description is complete. It explains the authenticated-user scope, the key fields returned, the model_hash convention that matters for all downstream calls, and all pagination and filtering parameters. Nothing an agent needs to invoke this tool correctly 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 for parameter semantics. It fully compensates by explaining include_unsaved as 'draft/unsaved models', limit as 'maximum number to return' with a max of 500, and offset as 'number of models to skip, for paging past limit.' This goes well beyond the bare schema properties.

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 verb and resource: 'List all Marketing Mix Models for the authenticated user.' This clearly identifies the operation and scope, and the resource name differentiates it from sibling list_* tools such as list_studies, list_uploads, and list_projects.

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 gives clear usage context by explaining that all other model endpoints use model_hash and instructing the agent to use the model_hash from this response. It does not explicitly name alternative tools or when-not-to-use conditions, but the hash-identifier guidance strongly implies this tool is the entry point for model operations.

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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