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set_llm_key

Set (or clear, with key=null) the LLM provider API key an instance uses to answer. Required before a freshly created instance can actually respond.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
keyYesThe provider API key. Stored encrypted, never echoed back.
modelNo
providerYesLLM provider id, e.g. openai, anthropic, deepseek.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds the key nuance that passing null clears the key, and that setting the key is a prerequisite for an instance to respond. However, it does not mention side effects like replacing an existing key or any error conditions, so it is not fully comprehensive.

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?

Two concise sentences front-load the core action and then provide the crucial usage context. There is no redundancy or filler.

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

Completeness4/5

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

For a simple setter tool with no output schema, the description covers the key purpose, the null-clearing behavior, and the prerequisite context. Minor gaps remain: model semantics are not defined, and the source for valid provider ids is not explicitly routed to list_platforms, but these are optional or inferable.

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 coverage is 50%: provider and key are documented in the schema, while id and model are not. The description adds semantics for key via the 'clear, with key=null' clause, but does not explain what id or model mean, leaving the undocumented parameters without sufficient guidance.

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 states a specific verb ('Set') and resource ('LLM provider API key'), and clarifies the clear behavior with key=null. This distinguishes it from siblings like set_peers, since it explicitly targets the LLM key rather than any other setting.

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 context: 'Required before a freshly created instance can actually respond', which tells the agent when this tool should be called relative to create_instance. It does not explicitly name alternatives or when-not-to-use, but the applicability is strongly implied.

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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