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OwlTing

OwlPay MCP Server

by OwlTing

search_owlpay_documentation

Search OwlPay API documentation and guides using natural language queries to accelerate system integration. Non-English queries are automatically translated to English.

Instructions

Search Owlpay documentation. Any non-English input will be auto-translated to English before populating the query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch keywords in English. Any non-English input will be auto-translated to English before populating this field.

Implementation Reference

  • Core handler function implementing the tool logic by making an HTTP request to the OwlPay documentation search API.
    def search_owlpay_documentation_func(query: str) -> str:
        """Call external Owlpay API and return raw text or raise McpError."""
        try:
            session = requests.Session()
            session.mount("http://", HTTPAdapter(max_retries=3))
            resp = session.get(
                SEARCH_API, params={"query": query}, timeout=30
            )
            resp.raise_for_status()
            return resp.text
        except Exception as e:
            raise McpError(
                ErrorData(
                    code=INTERNAL_ERROR,
                    message=f"Failed to search documentation: {e!r}",
                )
            )
  • Pydantic model defining the input schema for the search_owlpay_documentation tool.
    class SearchArgs(BaseModel):
        """Parameters for searching Owlpay documentation."""
        query: str = Field(..., description="Search keywords in English. Any non-English input will be auto-translated to English before populating this field.")
  • Registers the search_owlpay_documentation tool in the stdio MCP server, providing name, description, and schema.
    @server.list_tools()
    async def list_tools() -> list[Tool]:
        return [
            Tool(
                name="search_owlpay_documentation",
                description="Search Owlpay documentation. Any non-English input will be auto-translated to English before populating the query.",
                inputSchema=SearchArgs.model_json_schema(),
            )
        ]
  • FastMCP tool handler for search_owlpay_documentation, delegating to the core function.
    @mcp.tool(description='Search Owlpay documentation. Any non-English input will be auto-translated to English before populating the query.')
    def search_owlpay_documentation(
        query: Annotated[str, Field(description="Search keywords in English. Any non-English input will be auto-translated to English before populating this field.")]
    ) -> str:
        """Search Owlpay documentation with direct query parameter."""
        return search_owlpay_documentation_func(query)
  • MCP stdio server's call_tool handler that validates input using schema and invokes the core search function.
    @server.call_tool()
    async def call_tool(
        name: str, arguments: dict[str, Any] | None
    ) -> list[TextContent]:
        try:
            args = SearchArgs(**(arguments or {}))
        except ValueError as e:
            raise McpError(ErrorData(code=INVALID_PARAMS, message=str(e)))
    
        result = search_owlpay_documentation_func(args.query)
        return [TextContent(type="text", text=result)]
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions auto-translation of non-English inputs, which adds some context about query processing. However, it lacks details on response format, error handling, rate limits, or authentication needs, leaving significant gaps for a search tool with no output schema.

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 a single, efficient sentence that states the tool's purpose and key feature (auto-translation) without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 parameter, 100% schema coverage) and lack of annotations or output schema, the description is incomplete. It doesn't explain what the search returns, how results are formatted, or any behavioral traits like error cases. For a search tool, this leaves the agent with insufficient context to use it effectively.

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?

The schema description coverage is 100%, with the single parameter 'query' fully documented in the schema. The description adds value by reiterating the auto-translation feature for non-English inputs, which provides context beyond the schema's description. However, it doesn't introduce new parameter details, so it meets the baseline for high schema coverage.

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?

The description clearly states the tool's purpose: 'Search Owlpay documentation' with the specific verb 'search' and resource 'Owlpay documentation'. It distinguishes itself by mentioning auto-translation of non-English inputs, which adds specificity. However, with no sibling tools, there's no explicit differentiation from alternatives, preventing a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It mentions auto-translation as a feature but doesn't explain when this is beneficial or if there are limitations. With no sibling tools, context for usage is minimal, leaving the agent with little operational guidance.

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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