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raqueljezweb

AnythingLLM MCP Server

by raqueljezweb

update_llm_provider

Change the AI model provider and its settings for the AnythingLLM MCP Server to configure language model access.

Instructions

Update LLM provider configuration

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerYesProvider name (openai, anthropic, etc.)
apiKeyNoAPI key for the provider
modelNoModel to use
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Update' implies a mutation operation, but it doesn't specify whether this requires admin permissions, if changes are persistent or reversible, what happens to existing configurations, or potential side effects. For a configuration tool with zero annotation coverage, this leaves critical behavioral aspects unclear.

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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple update operation and front-loads the essential information. Every word earns its place.

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?

For a mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'configuration' entails beyond the parameters, what the update affects (e.g., default model, authentication), or what success/failure looks like. Given the complexity of updating provider settings, more context is needed for the agent to use this tool effectively.

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 100%, with clear descriptions for all three parameters (provider, apiKey, model). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

Purpose3/5

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

The description 'Update LLM provider configuration' clearly states the action (update) and resource (LLM provider configuration), which is better than a tautology. However, it doesn't specify what aspects of configuration are updated or distinguish this tool from sibling tools like 'update_system_settings' or 'update_workspace_settings' that might also involve configuration changes.

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?

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (e.g., needing existing provider setup), exclusions, or comparisons to related tools like 'list_llm_providers' or 'update_system_settings'. The agent must infer usage from the name alone.

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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