Simba MCP Server

Official

by getsimba-ai

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Update Run

update_run

Destructive

Rename and annotate optimizer or scenario runs with custom names, notes, and tags to make run history more descriptive and prevent auto-naming overwrites.

Instructions

Rename / annotate a saved optimizer or scenario run.

Runs are auto-named at creation (e.g. "$1.2M · 12mo · Jan 5"); renaming makes run history carry the analysis ("holiday cut -10%", "stretch 130%"). Renaming permanently flips the run's auto_named flag to false so future auto-naming never overwrites it. Only the fields you provide are changed.

Args: artifact: "optimizer" (run_id "opt_...") or "scenario" ("scn_..."). model_hash: Hash of the model the run belongs to. run_id: The run's stable id from run history. name: New display name (non-empty when given; capped at 255 chars). notes: Free-text annotation. Omit to leave untouched; pass "" to clear. tags: Replacement tag list (max 20 tags, 64 chars each).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No
`tags`	No
`notes`	No
`run_id`	Yes
`artifact`	Yes
`model_hash`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.5.0Sep 20, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "update_runDictOutput",
+  "type": "object"
+}

First observedv0.3.2Aug 28, 2026

TDQS

A4.8/5.0

Behavior5/5

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

The description goes well beyond the annotations by disclosing that renaming permanently flips the auto_named flag to false, that only provided fields are changed, and that notes can be omitted to leave untouched or passed as empty string to clear. This is valuable behavioral context that complements the destructiveHint and readOnlyHint annotations without contradicting them.

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 with a concise summary line followed by a clear Args list. Every sentence earns its place: the auto-naming example illustrates intent, the permanent flag note warns about irreversibility, and the parameter constraints are precise. There is no filler or redundancy.

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 all six parameters, the partial-update behavior, the permanent auto_named flip, and edge-case semantics for notes. Since an output schema is present and annotations already convey the read/write/destructive profile, an agent has everything necessary to invoke this tool correctly.

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?

With 0% schema description coverage, the Args section fully compensates by documenting every parameter: artifact values with run_id prefixes, model_hash, run_id, name constraints (non-empty, capped at 255), notes semantics (omit vs empty), and tags limits (max 20 tags, 64 chars each). The description adds significant meaning beyond the raw 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 opening line uses a specific verb-resource pair ('Rename / annotate a saved optimizer or scenario run'), which clearly identifies the tool's purpose and distinguishes it from sibling tools like set_run_pinned or update_recipe_draft. It also names the two artifact types (optimizer/scenario) and their run_id prefixes, leaving no ambiguity about what the tool operates on.

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 provides clear context for when to use the tool: runs are auto-named at creation and renaming makes run history carry the analysis, implying this tool is for annotating/renaming existing runs. It doesn't explicitly reference alternatives or exclusion conditions, but the context is strong enough that an agent can infer the appropriate use case.

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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