Whissle MCP Server

summarize_text

Condense lengthy text into concise summaries using AI models. Specify custom instructions to tailor summaries for different purposes.

Instructions

Summarize text using an LLM model.

⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.

Args:
    content (str): The text to summarize
    model_name (str, optional): The LLM model to use. Defaults to "openai"
    instruction (str, optional): Specific instructions for summarization

Returns:
    TextContent with the summary.

Input Schema

TableJSON Schema

Name	Required	Default
`content`	Yes
`model_name`	No	openai
`instruction`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes
`type`	Yes
`_meta`	No
`annotations`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Feb 2, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond basic functionality: it warns about API costs and specifies the external service ('Whissle'), which are crucial behavioral traits. It doesn't cover rate limits, authentication needs, or error handling, but provides valuable operational context.

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 well-structured and appropriately sized. It front-loads the core purpose, follows with a critical warning, and then details parameters and returns in a clear format. Every sentence earns its place, with no redundant or verbose language.

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

Completeness4/5

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

Given the tool's moderate complexity (3 parameters, no annotations, but with output schema), the description is reasonably complete. It covers purpose, cost implications, parameters, and return type. The output schema handles return values, so the description doesn't need to explain them. It could benefit from more parameter details or error scenarios, but it's largely adequate.

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 description coverage is 0%, so the description must compensate. It lists all three parameters (content, model_name, instruction) with brief explanations, adding meaning beyond the bare schema. However, it doesn't elaborate on parameter constraints (e.g., model options, instruction format) or provide examples, leaving some semantic gaps.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Summarize text using an LLM model.' This specifies the verb ('summarize'), resource ('text'), and method ('LLM model'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'translate_text' or 'speech_to_text' beyond the core function.

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?

The description provides clear usage guidance with the cost warning: 'Only use when explicitly requested by the user.' This establishes a specific context for when to use the tool. However, it doesn't mention when NOT to use it (e.g., for simple text extraction) or name alternatives among sibling tools.

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

Deploy Server