Turn up to 1,000 characters of text into natural speech as an audio file (base64, with its content type), in English, Spanish, French, Chinese, Japanese or Korean, with MeloTTS. Use for voice replies, audio notes, accessibility and phone or chat bots. Costs $0.003 in USDC on Base per successful call, paid with x402.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language: en, es, fr, zh, ja or ko (default en) | |
| text | Yes | Text to speak, up to 1,000 characters |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare the safety profile (not read-only, open-world, not idempotent, not destructive), so the description's job is to add context — and it does: per-call cost of $0.003 USDC on Base via x402, plus the return shape (base64 audio with content type). Pricing and payment mechanism are exactly the kind of operational detail annotations cannot convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three compact sentences, front-loaded with capability, then use cases, then cost/payment. Every clause carries information (limit, languages, engine, return format, price, chain, payment protocol); a touch dense but no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, and the description compensates by stating the return format (base64 plus content type). It also covers input limits, language options, engine, and the monetary cost of invocation. An agent has everything needed to call this correctly and anticipate the charge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description largely restates the schema (1,000-character limit, the six supported languages). It adds the output encoding detail (base64 with content type), which is useful but not a parameter-level clarification. Baseline 3 applies when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (turn text into speech), the resource (audio file), the input limit (1,000 characters), and the engine (MeloTTS). It is unmistakably distinct from directionally opposite siblings like transcribe and from the many ai_* text tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Names concrete use cases (voice replies, audio notes, accessibility, phone/chat bots), which gives the agent a clear context for selection. It stops short of explicit exclusions or naming an alternative (e.g. transcribe for the reverse direction), so it is clear context without routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.