service_info
Start here. What this server sells, what it will not do, and the exact limits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Start here. What this server sells, what it will not do, and the exact limits.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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, the description carries the burden of behavioral disclosure. It reveals that the tool will describe what the server sells, what it will not do, and its limits, implying read-only, informational behavior. It doesn't detail response format or side effects but is reasonably transparent for a service info 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 extremely concise, consisting of two short sentences that are front-loaded with the key instruction 'Start here.' Every word adds meaning, and there is no filler.
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 zero-parameter informational tool with no output schema, the description adequately covers the needed context: it tells the agent what to expect (service summary, limitations) and positions the tool as the initial step. Nothing essential is missing.
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, and the schema covers an empty object, so there are no parameter semantics to explain. The baseline score of 4 applies because there is nothing for the description to add.
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's purpose: it serves as the starting point for understanding what the server offers, its limitations, and exact limits. This distinguishes it from sibling tools that handle specific tasks like fetching videos or submitting jobs.
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?
'Start here' explicitly instructs the agent to use this tool first, setting clear usage context. It does not name alternative tools or specify when not to use it, but the directive to begin here is a strong usage guideline.
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.
Each tool has a distinct role in the video generation workflow: submit creates, get_job polls, fetch_video retrieves the result, list_jobs lists history, and the remaining tools cover voices, payment, service info, and feedback. There is no meaningful overlap between tools.
Most tools follow a verb_noun pattern (submit_video_job, get_job, fetch_video, list_jobs, list_voices), but payment_info and service_info use a noun_info pattern. This is a minor deviation; all names are clear, snake_case, and readable.
With 8 tools, the set is well-scoped for a video generation service. It covers job submission, monitoring, retrieval, listing, voice selection, and two informational endpoints, without unnecessary bloat or a feeling of incompleteness.
The core lifecycle is covered: submit, poll, fetch, and list jobs, plus listing voices and payment guidance. The only notable gap is the lack of job cancellation or update operations, but agents can work around this since the service is fire-and-forget.