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TextLite MCP Server

TextLite MCP Server

Official
by Mantri-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Deduplicate Texts

deduplicate_texts

Removes exact and semantically similar duplicate texts while preserving duplicate groups for review or downstream processing.

Instructions

Remove normalized-exact and semantically similar duplicates; preserve duplicate groups.

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": {
      "texts": "<texts>"
    },
    "name": "deduplicate_texts"
  }
}

Input Schema

NameRequiredDescriptionDefault
textsYes
similarity_thresholdNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose real behavioral content beyond the name: two distinct duplicate-detection strategies and the fact that duplicate groups are preserved in the result. However, it omits what 'normalized' covers (case, whitespace, punctuation), which member of each group is retained, and whether removal is lossy or reversible, which are significant gaps for a data-reducing operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with the core action front-loaded and the secondary guarantee appended after a semicolon. No filler or redundant restatement of the title. The trailing clause is terse to the point of being slightly cryptic, which keeps it from a 5.

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?

An output schema exists, so return values need not be explained, but for a two-parameter transform tool with no annotations and no schema descriptions the description leaves too much open. It never explains the threshold's effect or which representative survives deduplication, both of which materially change the call's outcome.

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%, so the description must compensate, and it only partially does. 'Semantically similar' hints that similarity_threshold governs the semantic mode, but no range, units, or effect of the 0.9 default is given, and the relationship between the threshold and the normalized-exact mode is unstated. The texts parameter is self-evident from the name but adds nothing.

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 names a specific verb (remove) and resource (duplicates among texts) and distinguishes two detection modes: 'normalized-exact' and 'semantically similar'. It also states that duplicate groups are preserved, which tells an agent the tool reports groupings rather than just a flat filtered list. It does not contrast itself with siblings such as semantic_similarity or cluster_entities, so it falls short of a 5.

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 explicit when-to-use or when-not-to-use guidance, and no alternative tool is named for cases where the agent wants similarity scores or clustering instead of removal. Usage is only implied by the name and the word 'Remove'. An agent must infer the context from the tool name alone.

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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