RoxyAPI Docs MCP Server (keyless, for coding agents)
Server Details
Keyless docs search so coding agents wire up RoxyAPI: every endpoint, param and SDK call.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 5/5 across 1 of 1 tools scored.
There is only one tool, so there is no possibility of confusion or overlap. Agents can unambiguously select search_docs for any documentation query.
The single tool uses a clear snake_case verb-noun pattern (search_docs), which is consistent and follows a predictable convention. With only one tool, naming is trivially consistent.
The tool count is low (1) but appropriate for the server's narrow purpose as a documentation search service. It feels slightly thin compared to typical multi-tool servers, but the scope justifies a single tool.
For a documentation search server, the tool is quite complete—it handles queries, returns ranked snippets with URLs, and covers endpoints, SDKs, auth, and guides. The main gap is lack of browsing or listing capabilities, but search alone covers most integration discovery needs.
Available Tools
1 toolsearch_docsARead-onlyInspect
Search the RoxyAPI knowledge base and get back ranked documentation snippets, each with a source URL. It covers API endpoints with their request and response fields, SDK usage for TypeScript, Python, PHP, C#, and the WordPress plugin, authentication and API keys, UI components, and step by step integration guides. Call this first whenever you need to integrate RoxyAPI into an app: to find which endpoint or SDK method to use, what parameters a call takes, how to authenticate, or how to wire a feature end to end. Pass the user question verbatim as query. If the first results miss, rephrase once and retry.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max hits to return. Default 10, capped at 25. | |
| query | Yes | User question or keywords. Free text. The whole question works better than guessed keywords. | |
| compact | No | Set true for the same data in a compact shape: arrays of same-shaped objects arrive columnar as {"__cols":[names],"__rows":[[values]]}. Lossless, typically 40 to 52 percent fewer tokens. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only and non-destructive, but the description adds important behavioral context: the scope of the knowledge base, the output structure (ranked snippets with source URLs), and the expectation that the query should be passed verbatim. This goes beyond what annotations provide and contains no contradictions.
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 multi-sentence but every sentence earns its place: it states the function, lists coverage areas, gives when-to-use guidance, and explains query strategy. It is well-structured with the core purpose front-loaded, and there is no 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 search tool with no output schema, the description sufficiently covers return format (ranked snippets with URLs), content scope, and troubleshooting behavior (rephrase/retry). Combined with the self-documenting input schema, the agent has all needed information to invoke the tool correctly.
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 input schema already describes all three parameters (query, limit, compact) with helpful details. The description strengthens this by explicitly instructing to pass the user question verbatim as `query`, which is not in the schema. This adds real semantic value for parameter usage.
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 uses a specific verb ('Search') and identifies the resource ('RoxyAPI knowledge base') and the return format ('ranked documentation snippets, each with a source URL'). It clearly distinguishes this tool from any potential alternative by stating it covers API endpoints, SDK usage, auth, and integration guides.
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?
Explicitly states when to call it ('Call this first whenever you need to integrate RoxyAPI into an app') and enumerates concrete use cases. It also provides retry guidance ('If the first results miss, rephrase once and retry'), which is actionable and valuable.
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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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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