Tool-use review
Created · Long Context
Deployment Verdict
Do not deploy in autonomous MCP pipelines, as the tool calls are not valid and the combined score of 27.75 is clearly below production level. The only positive finding is that no hallucination was detected.
Tool Execution Profile
The core problem lies not in knowledge discipline but in execution. P1 consistently sits at 35 — a level that supports neither reliable tool selection nor protocol-compliant execution. On the Web Search & Tool Selection test, which checks whether the model recognizes unprompted that a search is required rather than a direct fetch, it shows no dependable tool intelligence. On the URL Construction & Fetch test, which requires deriving the correct target URL from its own knowledge and then retrieving it cleanly, it remains at the same weak level. This points to a rigid or uncertain tool pattern rather than context-dependent selection. Critical for production: the tool call was not valid. This effectively blocks MCP integration, even when the verbal response still appears usable. No retry was required, so this is not a one-off formatting anomaly but a fundamental reliability deficit.
Synthesis Fidelity
How well does it condense tool results? Poorly. P2 sits consistently at 20 across all assets — including HTTP Fetch & Extract and Multilingual Search & Synthesis, where precise condensation of retrieved content, proper nouns, and dates would matter. The model does not appear to robustly convert content into concise, actionable working responses.
Does it stay within the tool result or fall back on training? Here the verdict is better. On the Honeypot EU License Research test, which checks whether current license restrictions are genuinely sourced from web results rather than training knowledge, no hallucination was detected. This is a trust signal. It demonstrates restraint, but does not compensate for the weak synthesis.
Error Resilience
On the 404 test, which measures whether the model remains transparent after a failed tool call or fabricates page content, it does not hallucinate. This matters for production. A model is allowed to fail on a tool call, provided it reports the failure clearly. That minimum discipline is present here. It raises the model to “not dangerous” — not to “production-ready.”
Operational Profile
Total 61.43s per run. Individual calls 5.71s and 4.53s. Operated locally, so infrastructurally favorable. For this performance tier, the overall latency is too high.
Conclusion & Recommendation
Suitable at most for supervised local setups in which an external orchestrator enforces tool selection, validates calls, and verifies responses. Not suitable for agentic research workflows, compliance pipelines, or MCP workflows in general where the model must independently select and correctly invoke tools. Anyone requiring local sovereignty combined with genuine tool reliability should use this model solely as a language post-processing module — not as a tool agent.
This analysis was generated automatically based on benchmark data. Model used: GPT 4.5 by OpenAI. The raw data and full methodology are documented in the GitHub project.
