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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.
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@@ -10,10 +10,17 @@ import (
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"strings"
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"github.com/lukaszraczylo/llm-testbench/internal/eval"
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"github.com/lukaszraczylo/llm-testbench/internal/llm"
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)
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// Test is one catalog entry: a fully self-contained prompt scored by a
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// deterministic Evaluator.
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//
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// When Tools is non-empty, the test exercises function-calling: the runner
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// advertises the tools (tool_choice auto), then serializes the model's tool
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// calls into the canonical JSON envelope the eval.ToolCalled/
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// ToolCallWithArgs/ToolSequence/NoToolCalled evaluators parse. The Eval for
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// such a test scores that envelope, not the model's free-text answer.
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type Test struct {
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Eval eval.Evaluator
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ID string
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@@ -22,6 +29,7 @@ type Test struct {
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Description string
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System string
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Prompt string
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Tools []llm.Tool
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MaxTokens int
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}
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