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Tapra MCP server

Tapra MCP server

Official
by tapra-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Tapra status

tapra_status
Read-onlyIdempotent

Checks Tapra API health, available models, current prices, output folder, and ffmpeg installation to diagnose connection issues before retrying failed tools.

Instructions

Check the Tapra connection: API health, available models, current prices, output folder, and whether ffmpeg is installed. Free. Call this first when a tool fails.

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": "tapra_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds value the annotations do not: 'Free' (no credit consumed) and the fact that it performs an environment probe including local ffmpeg presence, which explains why it is a useful failure diagnostic rather than just a health ping.

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?

Three short sentences, all front-loaded: what it checks, then cost, then the invocation trigger. No filler and nothing an agent would need to skim past.

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?

There is no output schema, and the description compensates by naming the contents of the result (health, models, prices, folder, ffmpeg). It does not describe the shape or keys of that payload, but for a diagnostic tool the enumerated contents are sufficient for an agent to know what it will learn.

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?

Zero parameters, so the baseline is 4. The description correctly implies a no-argument call and adds no misleading parameter expectations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Check') and resource ('the Tapra connection') and then enumerates exactly what is inspected: API health, available models, prices, output folder, ffmpeg presence. That enumeration makes it immediately distinguishable from every sibling, which are all audio/text transformation operations.

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

Usage Guidelines4/5

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

'Call this first when a tool fails' is an explicit, actionable trigger that routes the agent correctly during error recovery. It doesn't state when NOT to call it (e.g. that it should be skipped in the happy path), but the positive guidance is strong enough for correct selection.

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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