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

Retrieve an existing VOP verification verdict by report ID.

Use the report_id returned by verify_output to fetch full verdict details.

Args: report_id: The verification ID returned by verify_output (the report_id field). audience: Response detail level. "agent" (default) — machine-readable verdict for agent consumption. "customer" — adds human-readable explanations for presenting results to end users. Do not use "admin" — it is reserved for platform operators only.

Returns: dict with keys: report_id, verdict, score, tier, fail_codes, issued_at (ISO 8601), settlement (always None), agent_performance

Retrieve VOP performance statistics for an agent. Free tier.

Aggregates verdict counts and average score across this agent's verify_output history. Use to evaluate an agent's track record before relying on its output.

Args: agent_id: The agent identifier to look up.

Returns: dict with keys: agent_id, total_jobs (int), pass_count (int), partial_count (int), fail_count (int), pass_rate_pct (float, 0-100 — NOT a 0.0-1.0 fraction), avg_vop_score (float, 0-100).

List VOP verification records with optional filters and pagination. Free tier.

Returns verifications in reverse chronological order.

Args: agent_id: Filter by agent ID (optional). verdict: Filter by verdict (optional). Values: "PASS", "FAIL", "PARTIAL" tier: Filter by tier (optional). Values: "Platinum", "Standard", "Partial", "Risk" date_from: ISO 8601 date lower bound, inclusive (optional). Example: "2026-06-01" date_to: ISO 8601 date upper bound, inclusive (optional). Example: "2026-06-30" limit: Maximum number of records to return (1–500, default 50). offset: Pagination offset — number of records to skip (default 0).

Returns: dict with keys: total (int), limit (int), offset (int), items (list of verification dicts with keys: verification_id, agent_id, verdict, score, tier, fail_codes, created_at)

Submit feedback about this MCP server or a VOP verification result. Free tier.

Use after any tool call to report issues, request features, or share what worked well. Feedback is reviewed weekly and drives product improvements.

Args: rating: Satisfaction score 1–5 (1 = very poor, 5 = excellent). category: Feedback type. One of: "wrong_verdict" — VOP verdict seems incorrect for the output "feature_request" — request a new capability or parameter "bug" — tool raised an error or behaved unexpectedly "praise" — something worked especially well "other" — anything else comment: Description of the issue or suggestion (max 2,000 characters). tool_name: The MCP tool name this feedback is about (optional). Example: "verify_output", "check_verdict" report_id: The report_id from a specific verification (optional). Helps correlate feedback with the exact VOP result.

Returns: dict with keys: received (bool), feedback_id (str), message (str)

Dispute a VOP verdict and get a deterministic tamper check. Free tier.

Re-runs the SAME engine version on the same inputs and compares the recomputed evidence hash to the one stored at issue time. A hash match proves the stored score was not altered after issue (the dispute is about scoring quality, not tampering); a mismatch signals engine version drift or a stored-score discrepancy that warrants manual review.

You must supply the EXACT original task_description (as request_content) and result_content used in the disputed verify_output call — they are hash-checked against the stored verification, so reconstructed or paraphrased text will be rejected. There is a time window to appeal after a verdict is issued (window length is server-configured); appealing after it closes returns an error.

Args: verification_id: The report_id from the disputed verify_output/check_verdict call. request_content: The exact original task_description/instructions (must hash-match what was stored). result_content: The exact original output text that was verified (must hash-match what was stored). appellant: Who is appealing (optional free text, e.g. "agent", "customer", or an identifier). reason: Why the verdict is being disputed (optional, recommended).

Returns: dict with keys: appeal_id, verification_id, status ("OPEN"), appeal_window (dict: window_hours, issued_at, deadline, enforced), tamper_check (dict: hash_match (bool) — false does NOT necessarily mean tampering, see interpretation; original_evidence_hash, recomputed_evidence_hash, interpretation (str)), original (dict: score, verdict), recomputed (dict: score, verdict, confidence, review_recommended, review_reasons)

Retrieve an appeal's status and tamper-check result. Free tier.

Args: appeal_id: The appeal_id returned by submit_appeal.

Returns: dict with keys: appeal_id, verification_id, appellant, reason, original_score, original_verdict, original_evidence_hash, recomputed_score, recomputed_verdict, recomputed_evidence_hash, hash_match (bool), status ("OPEN"|"RESOLVED"), outcome (None|"UPHELD"|"OVERTURNED" — set once resolved), resolution_score, resolution_note, created_at, resolved_at

List bundled domain criteria templates showing what a well-specified verification rubric looks like. Free tier.

Args: domain: Filter by domain name (optional, e.g. "data_analysis"). Matches domain equality or a template_id prefix match.

Returns: dict with key: templates (list of dicts, each with template_id, domain, items (list of {id, name, weight}), pass_threshold, partial_range ([low, high]))

List Manager AI oversight alerts (incidents) for this account. Free tier.

Manager AI continuously watches for customer-behavior anomalies (repeat rework, repeat disputes) and verification-quality anomalies in the background, and raises an incident when something looks off. This tool is always scoped to the account behind this server's own API key — there is no customer_id/agent_id parameter, so it is impossible to query another tenant's alerts.

Args: domain: Filter by domain (optional). Values seen in practice: "customer_behavior", "vop_quality", "verification_audit". severity: Filter by severity (optional). Values seen in practice: "info", "low", "medium", "high", "critical". status: Filter by status (optional). Values: "open", "resolved". limit: Maximum number of incidents to return (1-500, default 50).

Returns: dict with keys: total (int), incidents (list of dicts with keys: incident_id, domain, severity, signal, evidence (dict), status, created_at)