ai-workers-mcp

ai_compare

Compare two plain texts to identify and highlight similarities and differences using a free LLM. Focus on aspects like tone or structure for targeted analysis.

Instructions

Compare two plain texts and highlight similarities and differences using a configured free LLM. Plain prose only — no code, secrets, or file paths.

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_a": "<text_a>",
      "text_b": "<text_b>"
    },
    "name": "ai_compare"
  }
}

Input Schema

Name	Required	Description
`text_a`	Yes	First text
`text_b`	Yes	Second text
`focus`	No	Optional aspect to focus the comparison on, e.g. 'tone', 'structure'

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jun 14, 2026

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions using 'a configured free LLM' but does not explain how the comparison is performed, whether results are deterministic, or what happens with large texts. Key behavioral context is missing.

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?

The description is two sentences long, front-loads the purpose, and contains no extraneous information. Every word serves a clear function.

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?

Despite moderate tool complexity and no output schema, the description does not explain the return format or how results are presented. Key details about behavior (e.g., output structure) are missing, leaving the agent under-informed.

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?

The input schema already describes all three parameters (100% coverage). The description adds 'Plain prose only' as a constraint on the text inputs, which provides some additional context beyond the schema, but not enough to raise the score above the baseline.

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's function: compare two plain texts and highlight similarities/differences. The verb 'compare' and resource 'plain texts' are explicit, and the purpose distinguishes it from sibling tools (e.g., ai_summarize, ai_rewrite) which do different tasks.

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

Usage Guidelines3/5

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

The description provides a specific content restriction ('Plain prose only — no code, secrets, or file paths'), but does not explicitly state when to use this tool versus alternatives or mention any prerequisites or conditions. The guidance is useful but incomplete.

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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