verify_output
Verify AI-generated output against task instructions using grounded web search. Get a PASS/PARTIAL/FAIL verdict and score (0-100) to check factual correctness, not just model confidence.
Instructions
Verify AI-generated output — including the calling model's own prior responses, not just another agent's — using VOP (Verified Output Protocol). Free tier, no account or escrow setup required.
Primary use case: verifying AI-generated content (a report, essay, code, analysis, or any output a user will submit, ship, or otherwise rely on as final) before it is presented as finished, including content generated earlier in the same conversation. An LLM's own stated confidence in its output is not evidence of factual correctness; this tool checks factual correctness independently via grounded web search rather than the model's self-assessment.
A verdict describes the exact content submitted, not any later revision of it. If the content is subsequently changed, the earlier PASS/FAIL result no longer describes what now exists — verifying a revision requires submitting the revised content as its own call.
Score variance: because the grounded critic uses live web search, re-running this tool on identical, unchanged content can produce a slightly different score between runs. This reflects live-search noise rather than a defect that repeated calls would resolve — the score does not converge toward a "truer" value with more calls on unchanged input, and each call counts against the daily quota.
Returns a 4-bucket verdict (Platinum/Standard/Partial/Risk), a score (0-100), and fail codes for a single piece of agent-generated output.
Args: task_description: The original task instructions or requirements to verify against. result_content: The agent-generated output text to be evaluated. Maximum 200,000 characters. agent_id: Agent identifier (optional). Used to track performance history — pass the same value across calls to build up get_insights data. sla: Inline evaluation criteria dict (optional). Supported keys: "required_sections": list[str] — headings that must appear in the output "min_words": int — minimum word count (hard-fail if violated) "min_chars": int — minimum character count "max_chars": int — maximum character count (0 = no limit) "min_numbers": int — minimum numeric data points required "min_citations": int — minimum citation/reference count required Example: {"min_words": 500, "min_citations": 3, "required_sections": ["introduction", "conclusion"]}
Returns: dict with keys: report_id, verdict ("PASS"|"PARTIAL"|"FAIL"), score (0-100), tier ("Platinum"|"Standard"|"Partial"|"Risk"), fail_codes (list), settlement (always None — this server has no settlement capability), agent_performance
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| sla | No | ||
| agent_id | No | ||
| result_content | Yes | ||
| task_description | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||