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config_set

Update webapp config. Pass keys: server_host, server_port, theme, auto_sync, notifications, llm.

Instructions

Update webapp config. Pass keys: server_host, server_port, theme, auto_sync, notifications, llm.

Return Format

Standard dict with keys: success, message, data

Examples

await call_tool("config_set", {"theme": "dark"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
llmNo
themeNo
auto_syncNo
server_hostNo
server_portNo
notificationsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already indicate this is a non-read-only, non-destructive mutation. The description adds value by specifying the return format (standard dict with keys success, message, data) and providing an example. However, it does not disclose whether updates merge with existing config or overwrite it, which is a significant behavioral gap for a config setter.

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?

The description is concise and well-structured: a one-sentence purpose, a return format section, and an example. It is easy to scan and contains no fluff, though the example could be considered optional given the schema.

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

Completeness3/5

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

With 6 optional parameters and no output schema shown in the prompt, the description includes return format and an example, which helps. However, it lacks information about update semantics (merge vs overwrite), default behavior, and side effects, leaving gaps for an AI agent to invoke it correctly without additional context.

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

Parameters2/5

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

The schema has no descriptions for the 6 parameters (0% coverage), so the description must compensate. It lists the key names (server_host, server_port, theme, auto_sync, notifications, llm) but does not explain their semantics or allowed values beyond what the schema already shows. This is insufficient compensation for the lack of schema descriptions.

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 clearly states the tool updates webapp config, uses a specific verb 'Update', and lists the distinct keys that can be set. This differentiates it from the sibling tool config_get, which is presumably for reading config.

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 that this tool is for updating webapp configuration, making the use case obvious. However, it does not explicitly mention alternatives or when not to use it, such as reading config with config_get, but the context is sufficient.

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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