get_platform_metricsRetrieve the list of available social media metrics per platform and their human-readable labels for analytics reference.
Reference: which metrics are available per platform (impressions, likes, …) and their human labels.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections.
v0.11.1Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Output schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"v0.4.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds that it's a reference and includes human labels, but it doesn't disclose any additional behavioral traits such as output format, pagination, or rate limits. Since annotations cover the core safety, the added value is modest.
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 description is a single, focused sentence that starts with 'Reference:' to immediately signal its purpose. It includes examples and the key detail about human labels, with zero wasted words. The structure is optimal for an agent scanning tool definitions.
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 parameterless reference tool with an output schema present, the description adequately explains what the tool returns (metrics per platform with human labels). It doesn't describe the exact output structure, but the output schema likely covers that. No critical information is missing for an agent to decide to call it.
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?
The tool has zero parameters, so the schema coverage is trivially 100% and there is nothing to explain. Per the calibration rule, a zero-parameter tool gets a baseline of 4, and the description correctly adds no unnecessary parameter guidance.
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 tool returns a reference of available metrics per platform, with examples and human labels. It distinguishes itself from analytics data tools by positioning it as a metadata/reference lookup rather than a data pull, so an agent can tell it apart from siblings like get_analytics or get_total_impressions.
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?
The description implies usage when an agent needs to know which metrics are available, but it doesn't explicitly state when to use this tool versus alternatives like get_analytics or get_total_impressions. There is no mention of exclusions or when not to use it, leaving the decision partly to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.