Stop Linkedin Monitoring
stop_linkedin_monitoringInput Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The monitor to stop (the label it was set up with). | |
| agent_id | Yes | The agent the monitor lives on. |
stop_linkedin_monitoring| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The monitor to stop (the label it was set up with). | |
| agent_id | Yes | The agent the monitor lives on. |
Changes observed during successful MCP inspections.
Input schema / properties / agent_idAdded value: +{
+ "description": "The agent the monitor lives on.",
+ "type": "integer"
+}Input schema / properties / task_idRemoved value: -{
- "description": "The task the monitor lives on.",
- "type": "integer"
-}Input schema / requiredPrevious value: -[
- "task_id",
- "name"
-]New value: +[
+ "agent_id",
+ "name"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations give readOnlyHint=false and destructiveHint=false, so the description is expected to convey behavioral traits. It adds key behavioral details: scheduled runs stop, discovered posts and queued comments are retained, and the monitor can be resumed with the same name. This goes beyond the annotations and provides meaningful side-effect context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The summary is compact, front-loaded with the core action, then covers retention and resumption in two short sentences. A separate returns section clearly states the return shape. Every sentence contributes value and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description is complete: it declares what happens, what is preserved, how to reverse the action, and what the response contains. There are no obvious gaps for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already defines both parameters fully. The description adds useful context like 'same name to resume' and 'label it was set up with,' but this mostly reinforces rather than adds substantial new meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action: stop a LinkedIn post monitor and prevent future scheduled runs. It also clarifies what is preserved and how to resume via setup_linkedin_monitoring, clearly distinguishing it from related monitoring tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains when to use the tool: to stop a LinkedIn post monitor that should no longer run. It explicitly points to setup_linkedin_monitoring as the way to resume, giving the agent a clear alternative path. It does not discuss other alternatives like get_linkedin_monitors, but the usage context is clear.
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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