Skip to main content
Glama
arr-mcps
by arr-mcps

qui_rename_rss_rule

Rename an RSS rule in a qBittorrent instance via the qui API by providing the instance ID, current rule name, and new name.

Instructions

Call qui's PUT /instances/{instanceID}/rss/rules/{ruleName}/rename endpoint. Pass path variables directly in arguments, query values in arguments.params, and a JSON request body in arguments.body.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argumentsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations present, the description carries the full burden of behavioral disclosure. It mentions the HTTP method (PUT), implying a write operation, but does not disclose side effects, permissions, idempotency, or error behavior. The generic argument-passing instructions add some invocation context but not sufficient behavioral transparency.

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 a single sentence that is relatively concise and front-loaded with the endpoint reference. It contains three instructional clauses packed together, which makes it dense but not overly wordy. Some clarification could improve readability, but it earns a good score for efficiency.

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?

Given the lack of annotations and the generic schema, the description is incomplete. It references the full API path, so the agent can infer required path variables, but it does not explain what those variables represent, what values are valid, or what the request body must contain. The existence of an output schema mitigates return-value explanation, but invocation details remain under-specified.

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 input schema is a generic 'arguments' object with no specific fields, and schema description coverage is 0%. The description mentions three argument categories (path, query, body) but does not specify the names or meanings of path variables (instanceID, ruleName) or the request body structure. This leaves significant ambiguity for the agent.

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?

The description explicitly names the PUT endpoint for renaming an RSS rule, which clearly distinguishes it from sibling tools like set_rss_rule and remove_rss_rule. However, it focuses on the endpoint mechanics rather than directly stating the functional outcome, making it slightly less direct than ideal.

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 such as set_rss_rule or remove_rss_rule. It only describes the call mechanics, leaving the agent without contextual cues for selecting this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/arr-mcps/qui-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server