Generates a complete, runnable code snippet in a specified programming language for a given Vonage API task. This is the preferred tool when the user explicitly asks for a 'code snippet,' 'example,' or 'code in a specific language' like Node.js, Python, or cURL.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A clear and concise description of the task for which the code snippet is needed (e.g., 'send an SMS', 'make a voice call'). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It adds value by describing the output as 'complete, runnable' and noting it is for a specified language, but it does not explain potential limitations, input expectations for language specification, or any side-effect profile. This is a moderate addition beyond the tool's name and schema.
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?
The description is two sentences long, front-loaded with the core action and output, and every sentence adds valuable context. It is efficient and well-structured, avoiding unnecessary fluff.
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?
For a simple one-parameter tool with no output schema, the description provides the essential context: what it does, when to use it, and output expectations. It could be more explicit about whether the query must specify the programming language, but overall it is sufficient for the tool's complexity.
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?
The schema has 100% coverage for the single 'query' parameter, so the baseline is 3. The description adds a hint that the language is relevant to the snippet generation, but it does not directly elaborate on how the query should be structured or that it should include the language. No extra parameter-level detail is provided.
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?
The description clearly states it generates a complete, runnable code snippet in a specified language for a given Vonage API task, using a specific verb and resource. It also distinguishes itself from sibling tools by noting it is the preferred choice when the user asks for a 'code snippet,' 'example,' or 'code in a specific language'.
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?
The description explicitly states when to use this tool (when the user asks for a code snippet, example, or code in a specific language), giving clear triggers. However, it does not explicitly mention when not to use it or name alternative tools, so it lacks a full exclusion clause.
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.
Each tool has a well-defined, distinct purpose: API reference for endpoint details, code generator for snippets, docs search for general queries, SDK info for SDK specifics, troubleshooter for errors, tutorial finder for tutorials, and use case examples for real-world applications. Despite some potential overlap between search, tutorials, and use cases, the descriptions clearly delineate when to use each.
All tool names follow the consistent pattern 'vonage_' followed by a descriptive noun or noun phrase (e.g., api_reference, code_generator, docs_search). This makes the tool set predictable and easy to understand.
With 7 tools, the server is well-scoped. Each tool covers a distinct aspect of Vonage documentation (search, reference, code, SDK, troubleshooting, tutorials, use cases), and there are no redundant or unnecessary additions.
The tool set provides broad coverage of the documentation domain: finding docs, looking up API references, generating code, getting SDK info, troubleshooting, locating tutorials, and exploring use cases. There are no obvious gaps that would hamper common documentation tasks.
