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TextLite MCP Server

TextLite MCP Server

Official
by Mantri-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Info

server_info

Retrieve server version, configured models, and input limits without loading models to verify configuration and capacity before text processing.

Instructions

Return server version, configured models and input limits (no models loaded).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "server_info"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
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 is implicitly a harmless no-argument read, but the parenthetical "(no models loaded)" is ambiguous — it could mean the call loads no models, or that the server currently has none — and nothing is said about cost, caching, or freshness.

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?

One front-loaded sentence with no filler. The trailing parenthetical is terse to the point of ambiguity, which is the only blemish.

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?

An output schema exists, so return values need not be described, and with no parameters and no side effects there is little else to disclose. The one residual gap is the unclear parenthetical about model loading.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the schema has nothing to explain and the description doesn't need to compensate. Baseline applies.

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?

Uses a specific verb ("Return") and enumerates the exact payload: server version, configured models, and input limits. That is enough for an agent to know this is a metadata/introspection tool, clearly unlike the content-processing siblings, though it doesn't explicitly say so.

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 statement of when to call this versus anything else, no prerequisites, and no note on how to interpret the results. An agent must infer that this is the capability-discovery call.

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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