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outrank-mcp

outrank-mcp

by NoBanks
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

generate_keywords

Queue keyword cluster research for a product on a broad topic with language support. Generate SEO keywords to enhance content strategy.

Instructions

Queue keyword cluster research for a product on a broad topic.

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": {
      "product_id": "<product_id>",
      "topic": "<topic>"
    },
    "name": "generate_keywords"
  }
}

Input Schema

NameRequiredDescriptionDefault
product_idYesThe unique website product ID from list_products
topicYesBroad topic keyword to research
languagesNoISO language codes, e.g. ['en']

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations provided, so description must disclose behavior. It says 'Queue' implying asynchronous operation but does not explain what happens after queuing (e.g., returns a job ID) or how to retrieve results. Lacks details on non-destructive nature or rate limits.

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?

Single sentence, front-loaded with action and object. No unnecessary words, efficient for an AI agent to quickly understand.

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

Completeness3/5

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

Tool is relatively simple (3 params, no output schema). However, description fails to mention return value or async behavior, leaving a gap for the agent to know how to use the result. Missing context on what 'queue' means for follow-up.

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?

Input schema covers all parameters with clear descriptions, and the tool description adds minimal extra meaning. Baseline 3 is appropriate as schema already provides parameter semantics.

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?

Description clearly states the action ('Queue keyword cluster research') and the target ('for a product on a broad topic'). It distinguishes from sibling tools like generate_article or list_products by focusing on keyword research.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. Does not mention when not to use or any prerequisites like authentication or credits, which are available via sibling tool check_auth_and_credits.

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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