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Tapra MCP server

Tapra MCP server

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by tapra-ai
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Arabizi ↔ Arabic

convert_script

Convert Tunisian text between Arabizi Latin digits and Arabic script in either direction, enabling readable Arabic or Latin transliteration for messages, transcripts, and content.

Instructions

Convert Tunisian text between Arabizi (Latin letters and digits, e.g. "3aslema, chna7welek?") and Arabic script, in either direction. Costs money per character.

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>",
      "direction": "<direction>"
    },
    "name": "convert_script"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
directionYes
max_cost_tndNoRefuse to run if the estimated cost is above this many TND. Defaults to TAPRA_MAX_COST_TND when set.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint=false, idempotentHint=false, openWorldHint=true), and the description adds a genuinely important non-obvious trait: the call costs money per character. It does not elaborate on how cost is estimated or reported, but the key warning is front-loaded.

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?

Two sentences, zero filler, with the conversion scope and example up front and the cost caveat last. Nothing is repeated from the title or schema.

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

Completeness3/5

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

There is no output schema, so the description should say what comes back (converted text only? a cost figure?), and it does not. For a simple three-parameter tool the core is covered, but the cost mechanics and return shape are left to inference.

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 33%: only `max_cost_tnd` is documented in the schema. The description partially compensates by explaining the Arabizi alphabet ('Latin letters and digits') and giving an example, which clarifies the `text` parameter, and 'either direction' maps to the `direction` enum. The 20000-character cap and cost-cap interaction remain undocumented.

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 gives a specific verb (convert) and resource (Tunisian text) plus both directions and a concrete Arabizi example, so the agent knows exactly what transformation happens. It stops short of naming the sibling `translate` to distinguish transliteration from translation, which is the one nearby confusion risk.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the domain example and the 'either direction' phrasing, but there is no explicit statement of when to pick this over `translate` or when not to use it. The agent must infer that Arabizi-to-Arabic script conversion is different from translating between languages.

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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