feat: function-calling (MCP-style) tests plus ultra-hard tasks

Adds real OpenAI function-calling support end to end and two test tranches.

Client (internal/llm): Request.Tools advertises tools with tool_choice
auto; Response.ToolCalls carries the model's calls, decoding each
function's JSON-encoded argument string into an object (malformed
arguments preserved raw with Decoded=false). Verified live: the gateway
round-trips tools and the qwen/deepseek tool-call parsers emit the
expected format.

Runner: when a test declares Tools, it serializes the model's tool calls
into a canonical JSON envelope and scores that, not the free text.

Eval (toolcall.go): ToolCalled, NoToolCalled, ToolCallWithArgs (subset
match, number/string coercion), ToolSequence, FirstToolIs, ToolNotCalled,
ToolArgValuesCover - all parse the envelope, no llm dependency.

Tests: 12 function-calling tests (tools/function-calling) covering tool
selection, argument extraction, enum constraints, chained and parallel
calls, no-tool-needed and missing-param abstention, destructive-tool
safety, and name-vs-description traps; 8 ultra-hard single-shot tasks in
existing subcategories. Ground-truth self-consistency tests assert every
documented answer and ideal tool envelope scores 1.0.

internal/tests/catalog_test.go

@@ -72,6 +72,7 @@ func TestAll_SubcategoryMinimums(t *testing.T) {
 		"security":    {"appsec", "crypto", "secrets"},
 		"delivery":    {"git", "containers", "release-engineering"},
 		"ai":          {"vector-search", "llm-integration", "rag"},
+		"tools":       {"function-calling"},
 	}
 
 	counts := make(map[string]map[string]int)