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klaket-mcp

klaket-mcp

by huseyinstif
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

klaket_get_result

Retrieve the processed video as markdown with timestamped transcript, scene list, and on-screen text. Only works when the job status is 'done'.

Instructions

Get the processed video as LLM-ready markdown: timestamped transcript, scene list with descriptions and on-screen text. Only works when the job status is 'done'.

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": {
      "id": "<id>"
    },
    "name": "klaket_get_result"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesJob id of a completed job

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.1

TDQS

A4.3/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses prerequisite (job done) and output format (markdown). Could mention idempotency or failure cases, but adequately informs agent.

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

Conciseness5/5

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

Two sentences, front-loaded with purpose and output, then condition. No unnecessary words, highly efficient.

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?

For a simple retrieval tool with one parameter and no output schema, the description fully explains what it does, what it returns, and when to use it. Sibling tools are distinct enough to avoid confusion.

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?

Schema covers 100% of parameter with description 'Job id of a completed job'. Description adds no extra semantics beyond schema; baseline 3 is appropriate.

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?

The description clearly states the tool retrieves processed video as LLM-ready markdown, specifying content (timestamped transcript, scene list). It distinguishes from siblings like klaket_ingest (ingestion) and klaket_job_status (status check).

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

Usage Guidelines4/5

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

Provides explicit prerequisite: 'Only works when the job status is done.' Does not mention alternatives or when not to use, but the condition is clear enough for usage guidance.

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

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