get_app_analyticsTraffic analytics for an app's published site over a chosen window: total views, unique visitors, daily series, top pages, top referrer domains and device split.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Window in days (1-90, default 7) | |
| app_id | Yes | The app ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context by detailing the exact output categories (daily series, top pages, referrers, device split), which informs the agent what the response will contain. It does not contradict annotations and adds non-redundant information about behavior.
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?
A single sentence that is well-structured, front-loading the main purpose ('Traffic analytics for an app's published site over a chosen window') followed by a concise list of returned metrics. No redundant words or filler; every phrase earns its place.
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?
The description is quite complete for a read-only analytics tool. It tells the agent what metrics to expect, and the annotations cover safety. Minor gaps include not specifying ordering for 'top pages' or whether the daily series is per day in the window, but these are not critical for calling the tool correctly. Given the moderate complexity and no output schema, this is sufficiently complete.
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 input schema has 100% description coverage for both parameters: 'app_id' is described as 'The app ID' and 'days' includes the range and default. The description does not add any extra parameter-specific details, but with high schema coverage, a baseline of 3 is appropriate. No further compensation is needed.
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 a specific verb ('get'), a specific resource ('app analytics'), and the scope ('published site over a chosen window'). It enumerates the exact metrics returned (total views, unique visitors, daily series, top pages, top referrers, device split), which strongly distinguishes it from sibling tools like get_app_detail or get_app_status.
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 provides context that the tool targets the published site over a chosen window, which implies when to use it (when analyzing traffic of the live site). However, it does not explicitly contrast it with alternatives like get_ab_test_stats or get_app_detail, nor does it give any exclusion criteria. The guidance is present but not explicit about alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes with detailed descriptions, but there are some overlapping pairs like read_app_file/read_app_files and create_entity_records vs seed_entity, which could cause misselection. Singular/plural variants and compatibility tools introduce minor ambiguity, but the majority are well-separated.
Tool names predominantly follow a consistent verb_noun pattern (e.g., create_app, get_entities, delete_secret). There are some variations like 'agency_create_client' and 'seed_entity' that deviate slightly, but the overall convention is predictable and readable.
With 82 tools, the server is far above the typical range and feels overwhelming. Even for a full platform API, the count is extreme and likely increases selection complexity. A more curated set would improve navigability without sacrificing capability.
The tool surface is exceptionally comprehensive, covering app lifecycle, file operations, entity CRUD, versioning, A/B testing, secrets, integrations, domains, agents, scheduling, policies, and member management. No obvious missing operations for the platform's scope; it even includes validation and workflow guidance tools.