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RoxyAPI Docs MCP Server (keyless, for coding agents)

by com.roxyapi

Server Details

Keyless docs search so coding agents wire up RoxyAPI: every endpoint, param and SDK call.

Ownership verified
Status
Healthy
Last Tested
Transport
Streamable HTTP
URL

Available Tools

1 tool
search_docsA
Read-only
Inspect

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoMax hits to return. Default 10, capped at 25.
queryYesUser question or keywords. Free text. The whole question works better than guessed keywords.
compactNoSet 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.

TDQS

A5/5.0
Behavior5/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters5/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool update
    • Changedsearch_docs2 fields changed
      • addedInput schema / examples
        Added value: +[
+  {
+    "query": "how do I authenticate an API request"
+  }
+]
      • changedInput schema / properties / compact / description
        Previous value: -"Set true to receive the exact same data in a token-optimized shape that is cheaper for you to read: whitespace is stripped and every array of same-shaped objects is encoded columnar as {\"__cols\":[field names],\"__rows\":[[values]]}, so each field name is sent once instead of once per row. Fully lossless (no field or value is dropped or changed) and typically 40 to 52 percent fewer tokens on large results. Prefer true whenever token or inference cost matters. Default false returns standard indented JSON."New value: +"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."
  2. 1 tool update
    • Changedsearch_docs1 field changed
      • changedInput schema / properties / compact / description
        Previous value: -"Return the same data in a token-optimized compact shape (minified, with same-shaped arrays encoded columnar) to reduce LLM token cost. Lossless: no fields are dropped. Default false."New value: +"Set true to receive the exact same data in a token-optimized shape that is cheaper for you to read: whitespace is stripped and every array of same-shaped objects is encoded columnar as {\"__cols\":[field names],\"__rows\":[[values]]}, so each field name is sent once instead of once per row. Fully lossless (no field or value is dropped or changed) and typically 40 to 52 percent fewer tokens on large results. Prefer true whenever token or inference cost matters. Default false returns standard indented JSON."
  3. 1 tool update
    • Changedsearch_docs1 field changed
      • addedInput schema / properties / compact
        Added value: +{
+  "default": false,
+  "description": "Return the same data in a token-optimized compact shape (minified, with same-shaped arrays encoded columnar) to reduce LLM token cost. Lossless: no fields are dropped. Default false.",
+  "type": "boolean"
+}
  4. 1 tool update
    • First observedsearch_docs

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TDQS

A4.9/5.0
Disambiguation5/5

There is only one tool, so there is no possibility of confusion or overlap. Agents can unambiguously select search_docs for any documentation query.

Naming Consistency5/5

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.

Tool Count4/5

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.

Completeness4/5

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.

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