LLMExpert: cited Q&A over YouTube videos

Get an ingest job

get_status

Read-only

Free. Get an ingest job started by transcribe or add_sources. Maps to GET /v1/jobs/{jobId}. Poll every 10–30 s until status is completed, partial or failed (terminal). completed means every accepted video is transcribed and indexed, so the collection is queryable; partial means some videos failed and were credited back (credited_usd); videos.completed / videos.failed count progress. Works with an API key or the collection's ct_ token as the bearer. Prices are the service defaults; quote returns the exact price.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "job_id": "<job_id>"
    },
    "name": "get_status"
  }
}

Input Schema

Name	Required	Description	Default
`job_id`	Yes	The `job_id` from `transcribe` or `add_sources`.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.6/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only supply readOnlyHint and openWorldHint, and the description adds substantial context beyond that: auth options (API key or the collection's `ct_` token as bearer), what `completed` vs `partial` mean operationally, and that failed videos are credited back via `credited_usd`. It also clarifies that prices are service defaults and `quote` gives exact pricing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the purpose and lifecycle semantics before the trailing pricing note. Dense but nearly every sentence carries operational value; the final pricing/`quote` remark is slightly tangential to a status poll.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description carries the burden of explaining returns, and it does: `status` values and their meanings, `credited_usd`, and `videos.completed` / `videos.failed` progress counters. Combined with auth guidance, an agent can call and interpret this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single parameter with 100% schema description coverage, so the schema already documents `job_id`. The description's 'from `transcribe` or `add_sources`' wording mirrors the schema text and adds no format or syntax detail, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Get an ingest job') plus the origin of the identifier ('started by `transcribe` or `add_sources`') and the underlying endpoint. This distinguishes it cleanly from sibling `get_source` / `get_transcript` and from the creation tools it pairs with.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit operating procedure: poll every 10–30 s until `status` is completed, partial, or failed. It also defines which states are terminal, which is exactly the when-to-use / when-to-stop guidance an agent needs for a polling endpoint.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LLMExpert: cited Q&A over YouTube videos tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?