List all tracked videos. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Results per page (1-100, default 20) | |
| offset | No | Pagination offset | |
| end_date | No | Filter: posted before (YYYY-MM-DD) | |
| platform | No | Filter by platform | |
| start_date | No | Filter: posted after (YYYY-MM-DD) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states 'Free,' which hints at credit usage, but does not disclose that this is a read-only operation, pagination behavior, or what fields are returned. This is insufficient for a list tool.
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 concise at just two short sentences ('List all tracked videos. Free.'), with no filler or redundant information. It is front-loaded with the core purpose.
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 tool has 5 parameters and no output schema, yet the description only mentions the basic function. It does not describe the return format, sorting, ordering, or pagination details, which are important for a list operation. The 'Free' note is useful but does not compensate for the missing output context.
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 all five parameters (limit, offset, start_date, end_date, platform) are already documented in the schema. The description adds no extra meaning to the parameters, but the schema provides adequate semantics, keeping this at baseline.
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 'List all tracked videos' clearly states the verb (list), resource (videos), and scope (all tracked), which distinguishes it from sibling tools like get_video (single video) and search_content (search). The addition of 'Free' is extra but does not harm purpose clarity.
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?
No guidance is provided on when to use this tool versus alternatives. For example, it does not mention that this lists all tracked videos without filtering by account or search, nor does it suggest using get_account_videos or search_content for specific contexts.
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.
Every tool targets a distinct resource and action: account vs video vs folder vs analysis vs radar. Even similar tools like analyze_video and get_analysis are clearly separated by creation vs retrieval.
Most names follow a verb_noun pattern (track_video, get_account, create_folder). Minor deviations like 'growth_trends' and 'radar_history' are noun phrases but remain readable and predictable.
26 tools is slightly above the ideal range, but the server covers a broad domain with distinct sub-areas (accounts, videos, folders, analytics, AI analysis, radar), so the count is justified rather than bloated.
Core lifecycle operations are covered: track/untrack accounts and videos, list/get details, analytics, folders, and AI analysis. Minor gaps exist (no delete folder, no remove-from-folder), but they don't break primary workflows.
