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TextLite MCP Server

TextLite MCP Server

Official
by Mantri-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Cluster Entities

cluster_entities

Groups related entity names or phrases into clusters, returning members, labels, representatives, and cohesion scores for entity consolidation and analysis.

Instructions

Cluster related entity names or phrases; output members, label, representative and cohesion.

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": {
      "entities": "<entities>"
    },
    "name": "cluster_entities"
  }
}

Input Schema

NameRequiredDescriptionDefault
entitiesYes
dedup_thresholdNo
similarity_thresholdNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden and does very little with it. It says nothing about determinism, performance on large lists, the effect of the two thresholds, or whether the operation modifies anything; it mostly lists return fields, which the output schema already covers.

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 front-loaded clause with no filler, and the output fields are packed into a tight list. It is efficient, though the same brevity is what leaves the parameters and behavior under-specified.

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 tool whose results depend heavily on two tunable thresholds, the description never explains what those thresholds trade off (over- vs under-merging). An output schema exists, so return values need not be explained, but the missing input semantics and usage guidance leave real gaps.

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 all three parameters rely on the description, which mentions none of them: entities, dedup_threshold and similarity_threshold are undocumented. The phrase 'entity names or phrases' loosely signals the entities array contents, but the two numeric thresholds and their defaults remain unexplained.

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 uses a specific verb and resource: 'Cluster related entity names or phrases', which is clear enough for an agent to know what the tool produces. It does not explicitly differentiate itself from nearby tools such as deduplicate_texts or extract_topics, 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 when-to-use guidance, no mention of alternatives like deduplicate_texts, and no note on when clustering is preferred over similarity or topic extraction. The only context is the implied 'entity names or phrases' input, which an agent could already infer from the name.

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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