Add `ClientSession.register_tool_schema()` for dynamic tool discovery patterns

Description

Problem

ClientSession.validate_tool_result() validates tool results against cached output schemas in self._tool_output_schemas, which is populated exclusively by list_tools(). When a tool is invoked that wasn't returned by list_tools(), two things happen:

1. A warning is logged: "Tool {name} not listed by server, cannot validate any structured content"
2. If the tool returns structuredContent with an outputSchema, validation is skipped entirely — a correctness gap.

This affects MCP servers that use dynamic tool discovery, where list_tools() intentionally returns only a small subset of available tools (e.g., meta-tools like a search tool) and actual tools are discovered at runtime via semantic search or catalog queries.

Use case

AWS Bedrock AgentCore Gateway is an MCP-compatible server that can host hundreds or thousands of tools. Rather than returning all tools in list_tools() (which would overwhelm LLM context windows), it exposes a built-in tool called x_amz_bedrock_agentcore_search. Clients call this tool with a natural language query, and the gateway returns matching tool definitions — including names, descriptions, input schemas, and output schemas. The client then invokes these discovered tools by name via call_tool().

At that point, the SDK doesn't recognize these tools because they never appeared in a list_tools() response. This triggers the warning and skips output validation. The only workaround today is accessing the private _tool_output_schemas dict directly.

AWS Prescriptive Guidance documents this "search function" approach as one of three canonical tool discovery strategies for MCP (alongside static definition and dynamic discovery via list_tools()).

Proposed API

A single public method on ClientSession:

def register_tool_schema(
    self, name: str, output_schema: dict[str, Any] | None = None
) -> None:
    """Register a tool's output schema for result validation.

    Use this when tools are discovered dynamically (e.g., via a catalog
    search API) and won't appear in list_tools() responses.
    """
    self._tool_output_schemas[name] = output_schema

Usage:

# Client discovers tools via semantic search (not list_tools)
search_results = await session.call_tool("x_amz_bedrock_agentcore_search", {"query": "data analysis"})

# Register discovered tools for proper validation
for tool in parse_discovered_tools(search_results):
    session.register_tool_schema(tool.name, tool.output_schema)

# Now call_tool validates structuredContent correctly, no warning logged
result = await session.call_tool("data_analysis_v2", {"input": "..."})

This is purely additive — no changes to existing behavior, no new data structures. It writes to the same dict that _absorb_tool_listing() already populates.

References

• Search for tools in your AgentCore gateway with a natural language query — Official docs for the x_amz_bedrock_agentcore_search tool
• Tool discovery - AWS Prescriptive Guidance — Documents "search function" as a canonical MCP tool discovery strategy
• aws-samples/sample-aws-ops-agentcore-gateway-search — Working example with 268+ tools behind semantic search, reduced to 10-15 per query

Inizia da ClientSession.validate_tool_result() e dal popolamento esistente di _tool_output_schemas in list_tools() e _absorb_tool_listing(). Aggiungi il punto di ingresso pubblico per la registrazione descritto nell’issue, quindi verifica che uno strumento rilevato dinamicamente possa essere registrato e che il suo structuredContent venga validato senza generare l’avviso relativo allo strumento mancante.