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set_workspace_models

Idempotent

Enable or disable AI models for a workspace. models is a partial map of model key -> boolean: unlisted models keep their current setting (pass replace: true to treat it as the complete map, disabling everything unlisted). Requires an owner or admin role. At least one available model must stay enabled. Models reported as available: false cannot be enabled (the surface is not delivering) — they may only be set false. Each enabled model spends answer-run budget per run according to its weight (see get_workspace_models).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsYesPartial map of model key -> enabled. Valid keys: chatgpt, perplexity, gemini, google-ai-mode, google-ai-overview, grok, bing-copilot, chatgpt-search, qwen, deepseek, llama, claude-sonnet, gpt-5-search, brave-leo, naver, duckduckgo, baidu
replaceNoWhen true, `models` is the complete map: any unlisted model is disabled
workspaceIdYesWorkspace ID — get the list from the list_workspaces tool

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnly=false, idempotent=true, destructive=false), and the description adds substantial behavior beyond them: role requirements, partial-vs-complete map semantics via replace, the invariant that one available model must stay enabled, the restriction that available:false models can only be set false, and budget consumption per weight. This is rich, non-obvious operational context.

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?

Front-loaded with the core action, then constraints in a dense but ordered paragraph; the crucial default-vs-replace distinction appears before the secondary constraints. No filler sentences, though it is longer than strictly necessary and could be segmented. Every clause carries operational weight.

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 mutating, nested-object tool with no output schema, the description covers role authorization, the partial/complete map, the enabling invariant, unavailable-model handling, and budget impact. Nothing an agent needs to call this correctly is missing.

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, but the description adds meaning beyond the schema: it states that unlisted models keep their current setting (the schema only spells out the replace:true case) and clarifies the replace semantics as a complete-map override. This disambiguates behavior an agent would otherwise have to infer.

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?

States a specific verb pair (enable/disable) and resource (AI models for a workspace), which is unambiguous against siblings like get_workspace_models (read-only) or update_brand. An agent can identify the operation without opening the schema.

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?

Gives prerequisites (owner or admin role), constraints (at least one available model must stay enabled), and routes to get_workspace_models for weights and availability. It lacks an explicit 'call get_workspace_models first' instruction, but the context is clear enough to select and use the tool correctly.

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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