ZapifyAPI MCP Server

extract_keywords

Extract main keywords from text with relevance scoring to identify key topics and support text analysis.

Instructions

Extract main keywords from text with relevance scoring.

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": {
      "text": "<text>"
    },
    "name": "extract_keywords"
  }
}

Input Schema

Name	Required	Description	Default
`max`	No	Max keywords to return (default 10)
`text`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 3, 2026

TDQS

B3.3/5.0

Behavior3/5

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

No annotations are present, so the description carries the burden. It discloses a behavioral trait ('relevance scoring'), which adds value, but it does not explain sorting, return format, or language behavior, leaving meaningful gaps.

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?

A single sentence with no filler, immediately stating the action and key feature. Every word earns its place.

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

Completeness2/5

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

No output schema or annotations, and the description doesn't explain return structure, ordering, or edge cases. For a tool that returns scored keywords, this leaves the agent guessing about what it will receive.

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

Parameters2/5

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

Schema description coverage is 50% (max has a description, text does not), and the description does not compensate. It adds no parameter-level meaning, relying entirely on the schema's minimal info.

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 uses the specific verb 'extract' with resource 'keywords from text', and adds 'relevance scoring' as a distinctive feature. This clearly separates it from sibling tools like extract_url_metadata or analyze_sentiment.

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 is provided on when to use this tool versus alternatives, nor any exclusions or when-not-to-use conditions. The agent is left to infer usage from the name and siblings.

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