get_analysis
Get existing AI analysis for a video. Free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| video_id | Yes | Video UUID |
Get existing AI analysis for a video. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| video_id | Yes | Video UUID |
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?
No annotations are provided, so the description carries the full burden. It mentions the tool is 'Free' but does not disclose behavior such as what happens when no analysis exists, response format, or any side effects. Minimal behavioral transparency beyond the read-only nature implied by 'Get'.
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?
Two sentences, one of which is a single word ('Free'). Extremely concise and front-loaded with the core purpose, no filler content.
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 one-parameter read tool, the description is adequate but lacks detail about the return value or the nature of the AI analysis. Since there is no output schema, more context would help an agent understand what to expect from the response.
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 coverage is 100% (video_id with description 'Video UUID'), so the schema fully documents the parameter. The description adds no extra meaning beyond what the schema already provides.
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 uses specific verb 'Get' and clearly identifies the resource as 'existing AI analysis for a video'. The word 'existing' distinguishes it from sibling tool analyze_video, which likely creates new analysis.
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 for retrieving previously generated analysis, but does not explicitly state when to use this tool versus alternatives like analyze_video. No direct guidance on when-not-to-use or exclusion cases.
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.