anythingllm-mcp

update_workspace

Change an AnythingLLM workspace's settings by slug to adjust prompts, temperature, chat mode, model, provider, topN, similarity threshold, and refusal response.

Instructions

Update a workspace's settings. settings is an object, e.g. {openAiPrompt, openAiTemp, chatMode, topN, similarityThreshold, chatModel, chatProvider, queryRefusalResponse}.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>",
      "settings": "<settings>"
    },
    "name": "update_workspace"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes
`settings`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Sep 27, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It says 'update' but never states whether the settings object is merged with existing values or replaces them wholesale, whether fields can be cleared, what permissions are required, or what a successful response contains. For a mutation tool with nested-object input this is a significant gap, and the example key list is the only real behavioral hint.

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?

Two compact sentences with no filler, and the purpose statement is front-loaded before the parameter hint. The example object is dense but earns its place given the 0% schema coverage.

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

Completeness2/5

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

This is a mutation tool with a nested object parameter, no annotations, and no output schema. The description omits merge/replace semantics, key types and ranges, permission requirements, and any error behavior, leaving real gaps for such a complex input.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% with two parameters, so the description must compensate. It does helpfully enumerate the keys accepted inside the opaque 'settings' object (openAiPrompt, openAiTemp, chatMode, topN, etc.), which is meaningful beyond the bare 'type: object' schema. However the required 'slug' parameter goes entirely unexplained, and the example gives no types or acceptable ranges for those keys.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (update) and resource (a workspace's settings), which cleanly separates it from siblings like create_workspace, delete_workspace, and get_workspace. It does not, however, differentiate between settings updates and the closely related update_embeddings, so sibling routing is left partly to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool, no prerequisites (e.g. workspace must exist, caller must own it), and no reference to alternatives such as create_workspace for new workspaces. The agent gets a purpose but no selection guidance.

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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