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TextLite MCP Server

TextLite MCP Server

Official
by Mantri-AI

Summarize Text

summarize_text

Extracts key source sentences and returns them as an extractive summary, helping you condense long text while preserving the original wording.

Instructions

Produce an extractive (not generative) summary with selected source sentences.

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": "summarize_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
diversityNo
max_tokensNo
max_sentencesNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden, and it does disclose a meaningful trait: the summary is built from selected source sentences rather than generated prose, so output is verbatim-extractive. It says nothing about cost, limits, or determinism, though the presence of an output schema removes the need to describe return values.

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 front-loaded sentence with no wasted wording; the parenthetical '(not generative)' is the highest-value token in the definition and is placed immediately after the key term.

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?

For a four-parameter tool with zero schema description coverage, the definition is too thin: the agent learns the method but nothing about how diversity, max_sentences, or max_tokens shape the result. Output schema coverage mitigates the return-value side, but the input side is essentially undocumented.

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 0% across four parameters (text, diversity, max_tokens, max_sentences), and the description does not name or explain any of them. 'Selected source sentences' faintly gestures at sentence selection but gives no meaning for max_sentences, diversity, or max_tokens, leaving the agent to guess from bare names and defaults.

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?

States a specific verb and resource (produce a summary of text) and adds the crucial qualifier that the output is extractive rather than generative, which tells an agent what kind of result to expect. It does not name or distinguish itself from the nearby sibling summarize_paragraphs, so it falls short of full sibling differentiation.

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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives such as summarize_paragraphs or compress_contexts. The only implicit guidance is the 'extractive (not generative)' constraint, which describes the method rather than when to choose this tool.

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