Skip to main content
Glama
wizerai1111

Swarmrails MCP

by wizerai1111
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

bittensor_tts

Convert text to speech using Bittensor's decentralized AI network, generating base64 MP3 audio output for integration with AI assistants.

Instructions

Text-to-speech via Bittensor subnet 16. Returns audio as base64 MP3. Cost: $0.025 per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText to convert to speech

Implementation Reference

  • src/index.ts:285-287 (handler)
    The handler for bittensor_tts uses the callGateway helper to send the prompt to the Bittensor TTS route.
    case "bittensor_tts":
  result = await callGateway({ route: "bittensor-tts", prompt: a.prompt });
  break;
  • src/index.ts:159-169 (schema)
    Definition and input schema for the bittensor_tts tool.
      name: "bittensor_tts",
  description:
    "Text-to-speech via Bittensor subnet 16. Returns audio as base64 MP3. Cost: $0.025 per call.",
  inputSchema: {
    type: "object",
    properties: {
      prompt: { type: "string", description: "Text to convert to speech" },
    },
    required: ["prompt"],
  },
},

Tool Definition Quality

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It successfully discloses output format (base64 MP3) and cost structure, but omits other behavioral traits like potential latency, rate limits, error handling, or side effects of the network call.

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?

Extremely efficient three-clause sentence structure. Front-loads the core function (Text-to-speech), follows with output format, and ends with cost. Zero redundant words.

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?

Appropriately complete for a simple single-parameter tool. Compensates for the missing output schema by specifying the return format (base64 MP3) and includes cost transparency. Could benefit from brief error handling mention.

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?

Input schema has 100% description coverage for the single 'prompt' parameter. The description does not add parameter-specific semantics, meeting the baseline expectation for high-coverage schemas.

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?

Clearly states the tool performs text-to-speech using Bittensor subnet 16. Mentioning 'Returns audio as base64 MP3' effectively distinguishes it from sibling tools like bittensor_image, bittensor_video, and bittensor_text which handle different modalities.

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?

Provides cost information ($0.025 per call) which aids in usage decisions, but lacks explicit guidance on when to choose this over other bittensor tools or prerequisites for the prompt parameter.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/wizerai1111/swarmrails-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server