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deploy_ramp_dashboard

Deploy a performance analysis dashboard to Grafana for monitoring sensor data. Optionally add baseline comparison panels by specifying a build and profile for performance benchmarking.

Instructions

Deploy the RAMP Performance Analysis dashboard to a sensor's Grafana. Optionally patch with baseline comparison panels by specifying a build and profile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sensorNoSensor hostname. If omitted, uses first discovered sensor.
compareNoBuild name from baselines.json to compare against
profileNoProfile name (e.g., "All/No", "Base/Yes"). Required if compare is set.
Behavior2/5

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

With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions deployment and optional patching but doesn't disclose whether this is a read-only or destructive operation, what permissions are required, how errors are handled, or what the output looks like. For a deployment tool with zero annotation coverage, this is a significant gap in transparency.

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 efficiently structured in two sentences: the first states the core purpose, and the second adds optional functionality. Every word earns its place with no redundancy or fluff, making it easy to parse and understand quickly.

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 complexity of a deployment tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'deploy' entails (e.g., creates new dashboard, overwrites existing), what happens on success/failure, or return values. For a tool that likely modifies Grafana state, more behavioral context is needed to be fully helpful.

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%, so the schema already documents all three parameters thoroughly. The description adds marginal value by implying that 'compare' and 'profile' work together for baseline comparisons, but doesn't provide additional syntax, format details, or examples beyond what the schema specifies. Baseline 3 is appropriate when schema does the heavy lifting.

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 clearly states the action ('deploy') and target ('RAMP Performance Analysis dashboard to a sensor's Grafana'), with an optional enhancement ('patch with baseline comparison panels'). It distinguishes from siblings like 'update_dashboard' by focusing on deployment of a specific dashboard rather than general updates. However, it doesn't explicitly contrast with 'generate_dashboard_url' or 'restore_dashboard_version', which slightly limits differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when deploying a dashboard with optional comparison features, but provides no explicit guidance on when to choose this tool over alternatives like 'update_dashboard' or 'generate_dashboard_url'. It mentions the optional patching but doesn't specify prerequisites or exclusions, leaving usage context partially inferred.

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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