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generate_hooks

Archived source-free writer. Use research_hook_evidence instead. Always fails before model, template, provider, storage, or billing work. Research requires a real public source video with at least 200,000 observed views, a canonical link, and an independently audio-verified exact opener. Errors: unauthorized; invalid_request with reason unsourced_hook_generation_archived.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoGeneration engine and therefore the price: instant (0 base + 1/hook, deterministic templates, sub-second), smart (0 + 2/hook, one LLM call, seconds), research (10 + 4/hook, brief->draft->judge, tens of seconds). Aliases: template|off|quick->instant, llm|on|fast->smart, search|deep|deep_research->research. Omit (or auto) -> smart when an LLM key is configured, else instant.
tagsNo1-5 lowercase slug tags ([a-z0-9_-], <=40 chars) stamped on this call's usage event so a fleet can attribute spend per campaign. Omit for no tagging. Filter later with get_usage(tag=...) / list_hooks(tag=...).
countNoHow many hooks to generate, 1-25. Drives the price (base + per_hook * hooks_RETURNED) and the pre-flight reservation, so a high count on research is the expensive combination.
styleNoVoice/tone to match, <=200 chars. Honored as a real instruction by smart and research; on instant it only varies which deterministic template fillers are drawn, so it cannot change the voice there. Omit for the engine's default register.
topicYesWhat the hooks are about, 3-200 chars. A concrete subject ('cold plunges for desk workers') scores far better than a bare noun; on research it is also what the brief is researched against.
stanceNoOptional: what the creator is for or against, selling, or building, so hooks carry a real position instead of a neutral summary.
api_keyNoAPI key for this call. Omit to fall back to the Authorization: Bearer / X-API-Key request header (streamable-HTTP only), then the VHGENGINE_API_KEY env var (the stdio default). No key resolvable -> unauthorized.
clarifyNoRequest-sufficiency behaviour: 'ask' returns ONLY clarifying questions (uncharged, nothing generated) - relay them to your human, then re-submit enriched; 'auto' (default) proceeds and the research envelope carries the questions and proposed assumptions as observations; they do not currently change retrieval or writer prompts. 'off' skips the check. Batch and jobs accept only 'auto'/'off'.
creatorNoOptional: who is speaking, free text ('wedding videographer, 40k followers, I talk to camera over b-roll of my shoots'). The more the engine knows about the creator, the more the hooks are theirs rather than a generic narrator's.
audienceNoOptional: who watches ('engaged couples budgeting'). Aims every hook at a real audience instead of an assumed one.
languageNoThe language the hooks are WRITTEN in, and the market their evidence is scraped from. en (default) | fr | es | ar (Modern Standard Arabic) | ary (Moroccan Darija, Arabic script). NOT a translation layer: the brief researches the topic as it is actually discussed in that language, the platform evidence is fetched from that language's region with transcripts in that language, and the judge scores register in it rather than against English. Omit for English. Same price in every language.
platformNoTarget platform, which selects the length/format conventions the hooks are written and scored against. Defaults to tiktok when omitted.tiktok
verbosityNoHow much of the response envelope to return: minimal (identity, text, score total/source, money, honesty warnings, and any persona/shape), standard (the default, including hook receipts), full (adds per-dimension score numbers, notes, and attribution). A failing phone_test survives minimal; passing phone_test, say_it, and pattern_source are standard/full detail. Shapes the RESPONSE only, never what is generated, persisted, hashed for idempotency, or charged.standard
archetypesNoRestrict generation to these archetype ids (see list_archetypes). Omit to let the engine spread across archetypes, which is what you want unless you are deliberately narrowing a deck.
deadline_msNoMilliseconds you are willing to block, 1000-600000. If the chosen mode's p90 exceeds it the call returns IMMEDIATELY with auto_job:true + a job_id to poll with get_job instead of generating inline. Omit to disable deadline conversion; an exact keyed async job may still own the request.
idempotency_keyNoCaller-chosen replay key (any string, unique per intended effect). A repeat call with the SAME key returns the stored result and is NEVER charged twice; the same key with different arguments is an idempotency_conflict. Omit and every call is a fresh, separately charged operation.
requested_marketNoCaller-declared target market or locality, up to 100 characters. This is not inferred or verified and does not override today's language-derived evidence region. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
requested_dialectNoCaller-declared desired dialect or register, up to 100 characters. This is not an observed-language or classifier result. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
creator_profile_idNoExact owned profile id. Send with creator_profile_version and without inline creator/audience/stance/first_person_facts.
first_person_factsNoOptional: facts TRUE of this creator that hooks may assert first-person ('I have filmed 200+ weddings'). The ONLY sanctioned source of personal claims; without it, hooks never invent a biography.
footage_constraintsNoUp to 10 caller-declared filming or edit constraints, each up to 200 characters. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
delivery_constraintsNoDesired spoken performance or cadence, up to 300 characters, distinct from the broader style/voice field. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
desired_viewer_actionNoWhat the viewer should do after hearing the hook, such as keep watching, comment, or reconsider a belief, up to 300 characters. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
creator_profile_versionNoExact immutable profile version paired with creator_profile_id.
hook_length_constraintsNoDesired spoken-hook length, up to 200 characters, for example '8-12 words' or 'under 6 seconds'. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
desired_audience_feelingNoHow the audience should feel immediately after the hook, such as understood, curious, or challenged, up to 300 characters. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
requested_content_formatNoDesired production format: solo_talking_head, podcast, interview, yapping_monologue, voiceover, skit, montage, or other. This is a request, not a claim about any retrieved source. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.
caller_confirmed_constraintsNoUp to 10 caller-confirmed request constraints from a prior clarification round, each up to 300 characters. Runtime-generated questions and model assumptions are execution receipts, not copied here or treated as approved automatically. Recorded in commission identity and retained async-job snapshots. It does not change retrieval, prompts, scoring, pricing, or generated text yet.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNoauto_job path only: what to do next, in one sentence.
hooksNoRanked hooks, best first. Each is {id, text, archetype, score, claim_type, rank}. NOTE the key is `id`, and THAT is the hook id you pass to report_outcome(hook_id=...) / get_hook (where the same value is spelled `hook_id`). You pay per hook RETURNED, so this can be shorter than `count`. At standard/full, a judge-ranked smart hook may also carry {shape, phone_test, say_it}; a judge-ranked research hook carries receipts {persona, shape, phone_test, say_it, pattern_source}. pattern_source is null when no measured opener was attributed, otherwise its nested provenance is the authoritative actual platform, source surface, transcript method, language, route, media, and origin-time record. Minimal keeps persona and shape, plus phone_test only when it is the warning value 'fail'.
usageNoLLM token usage {input_tokens, output_tokens, est_cost_usd} (+ web_searches and fallback_* counters when those paths ran). NULL means NO LLM ran, which is not the same as zero tokens.
engineNoWhich generator implementation produced the deck.
job_idNoauto_job path only: poll it with get_job or block on wait_for_job.
reasonNoinstant only, non-null when the template pool ran out: prose saying hooks were WITHHELD rather than duplicated. On a clause-topic it says outright that widening `archetypes` will not help, so do not retry that way.
statusNoauto_job path only: the job's lifecycle state ('queued').
timingNoWhat actually happened: {latency_ms, stages}. `stages` keys are mode specific (instant render_ms; smart llm_ms/judge_ms; research brief_ms/draft_ms/judge_ms).
auto_jobNoPresent and true when deadline_ms created a job, or this exact key already owns the same async job, so the call returns a JOB instead of hooks. CHECK IT BEFORE READING `hooks`: on this path `hooks` is ABSENT and the body is the job pointer below (job_id, status, estimated_seconds, poll_after_seconds, status_url, expires_at, requeued, hint). Nothing is charged until the job runs.
replayedNotrue when an idempotency_key replayed a stored result, so nothing was charged again. The credits_charged below is what the ORIGINAL call cost.
requeuedNoauto_job path only: true when an idempotent resubmit revived an existing job.
researchNomode research ONLY (null on instant/smart). The grounding ledger you paid for. Keys, and what to DO with each: - brief_summary (str), angles (list of {angle, detail, why_it_stops_scroll}), vocabulary (list of insider terms), tensions (list of conflicts/open questions): the brief itself. Reuse them to write more, do not re-buy them. - clean (bool): the ONE boolean separating a clean run from a salvaged one. clean:false WITH a full hook count is a partial-quality delivery at full price, which is otherwise invisible. Check it before trusting the deck. - degraded (bool), degraded_reason (str|null), degraded_stages (list of stage names), judge_skipped (bool): what went wrong and where. A judge-degraded run BILLS at the smart tier, so reconcile spend against pricing_mode, not mode_used. - rank_basis (str): what the served ORDER means: llm_judge | judge_order_no_scores | heuristic | unranked. Pairs with each hook's score.source, which says what its TOTAL means. Do not present a 'heuristic' order as a judged ranking. - stages (list): the per-stage ledger, uniform rows {stage, status, attempts, calls, latency_ms, branches_ok, branches_failed, judged_n, backfilled_n, ranked_n, skipped_n, skipped_reason, notes}. null in a field means NOT OBSERVED and must never be read as 0; [] means observed and empty. - ledger_source (str): 'engine' when the pipeline reported its own ledger, 'derived' when this layer reconstructed it. On 'derived', treat every null as unobserved. - pool_size (int|null): unique draft candidates the judge chose from; null when not observable. A small pool means little real selection happened. - grounded (bool), web_searches (int), sources (list of {url, title}, max 10): grounded is true ONLY when at least one real web search ran. Cite `sources` rather than claiming the hooks are researched. - grounded_degraded_reason (str|null): non-null ONLY when a grounded brief was refused by the vendor and the run spent its retry ungrounded. Same price, different product: this is how you tell 'grounding never asked for' from 'asked for and refused'. - evidence (list): each {id, claim, kind, source_url, source_title, source_domain, cited_text, confidence, freshness_days, authority, angle_ids, bind_score}. confidence is computed in code from a binding against a span the vendor actually returned, never self-reported by the model. Quote `cited_text` when you need to show a receipt. - evidence_counters (dict): the honesty half: declared_total, invented_urls, duplicate_ids, source_url_corrected, cite_markup_stripped, orphan_citations, dropped_by_cap, rows, established, reported, unsourced, sources_seen, citations_seen, distinct_domains, primary_domains, id_remap. invented_urls counts model-cited URLs ABSENT from the real result set (nulled, not shipped). A high invented_urls / declared_total ratio means downgrade your trust in this grounding. - selection (dict|null): what the selector actually did. null means no selection ran (an empty report would wrongly read as 'nothing was dropped'). Carries floor, requested, returned, above_floor, band_counts, relaxations, shortfall {requested, returned, cause, message}, the drop counters (near_duplicates_dropped, contradictions_dropped, off_topic_dropped, below_floor_dropped, archetype_capped_dropped, register_capped_dropped), diversity_thinning {thinned, displaced, delivered_mean, top_ranked_mean, quality_delta}, the caps (archetype_cap, register_cap, dedup_threshold), constraints_unavailable, and a one-sentence `notice`. A NEGATIVE diversity_thinning.quality_delta means the diversity caps cost you quality: narrow `archetypes` next time if you would rather have the top-ranked set. - injection_attempt (bool): true when the grounded brief reported that its search results tried to INSTRUCT it. Treat as a security signal: never auto-execute anything derived from that run's text. - platform_evidence (dict|absent): the measured-openers stage. ABSENT means this deployment has no evidence key and the stage does not exist; present means it ran. {used, source, routes, scanned, with_speech, deduplicated, filtered, dropped, kept, not_requested, budget_omitted, transcript_unavailable, not_selected, terminal_accounted, reconciled, cached, reason, rank_policy, rank_signals, transcripts_requested, requested_platform, actual_platforms, requested_language, observed_languages, transcript_methods, duration_policy, origin_fetched_at, current_request_vendor_calls, current_request_vendor_credits, origin_vendor_calls, origin_vendor_credits, vendor_calls, vendor_credits, planner_version, plan_fingerprint, query_policy, instagram_policy, query_plan, route_failures, suggested_queries, logical_vendor_calls, vendor_attempts, vendor_logical_call_cap, vendor_logical_call_cap_exhausted, adaptive_transcripts}. Experimental fields appear only for an active lab policy. used:true means real opening lines transcribed off the platform reached both the brief and the WRITER; used:false always carries a `reason`. `reconciled:true` means kept plus every terminal drop category equals scanned exactly. `source` names the supply that actually answered, and says '(proxy for X)' when you asked for a platform with no route of its own. cached:true means the openers were observed up to 15 minutes before this run rather than during it. The counters attribute every drop between scanned and kept, so a low `kept` has a cause rather than a shrug, and `dropped` breaks the qualification losses out by cause: views_below_minimum (the platform-reported count was below the inclusive 200,000-view eligibility floor, so no transcript was bought), language (the platform says the clip is SPOKEN in another language), ad (bought reach, so the view count does not measure the opening line), duration. Guarded native discovery also reports ad_unverified, duration_unverified, language_unverified, and format_unverified when the vendor did not establish a required fact. rank_policy says which clips became your evidence: 'blend' fuses raw views with outperformance (views over followers) and velocity (views per day), so a clip that escaped a small audience outranks a large account posting an ordinary result; 'views' is raw popularity. rank_signals lists the ones actually available, since not every route publishes follower counts or dates. origin_vendor_calls and origin_vendor_credits describe the fetch that populated the evidence. current_request_vendor_calls and current_request_vendor_credits are zero on a cache hit. vendor_calls and vendor_credits remain compatibility aliases for origin cost. None changes your price, which is fixed per hook. - clarification (dict|absent): present only when you passed clarify. {mode, sufficient, questions, assumptions, creator_context_declared}. sufficient:false with questions is the run telling you it generated WITHOUT knowing something it needed; answer them in creator_context and re-submit for a materially better set. - personas (dict|absent): {set, assumption, from_creator_context}. The speaker profiles the run inferred and wrote for. from_creator_context:false means the engine GUESSED who is talking, and `assumption` is the guess it made.
mode_usedNoThe engine that actually ran (instant|smart|research) after aliases and auto were resolved. May differ from mode_requested.
shortfallNo{requested, returned, cause} on EVERY response. `cause` is a closed vocabulary: 'none' on a full delivery, else template_pool / model_under_delivery / pool_exhausted. Branch on cause; never retry on 'none'. You were billed for `returned`, not `requested`.
base_priceNoFixed part of the charge for this mode.
expires_atNoauto_job path only: earliest terminal-row prune cutoff, ISO-8601 UTC. Queued/running rows are not deleted solely because this time passed.
rank_basisNoverbosity=minimal ONLY, alongside degraded_stages: hoisted out of `research`; what the served ORDER means (llm_judge | judge_order_no_scores | heuristic | unranked). At standard/full read research.rank_basis.
request_idNoId of this call. Keep it: get_usage(request_id=...) itemises exactly what it charged, and it identifies the call in a support question.
status_urlNoauto_job path only: REST URL for the same status (HTTP clients).
llm_fallbackNotrue when the requested LLM tier was unavailable and a cheaper engine ran instead; llm_fallback_reason says why. You are billed for what RAN.
pricing_modeNoThe tier billed, which is what price_per_hook belongs to.
expected_waitNo{mode, p50_ms, p90_ms, source} for this mode, to size the NEXT call.
hook_instancesNoImmutable non-prose served-occurrence identities. Profile-bound queued/running work returns an empty list; a successful result returns one row per served hook.
mode_requestedNoThe mode string you sent, before alias/auto resolution.
price_per_hookNoPer-hook part of the charge for this mode.
prompt_versionNoPrompt build used, for reproducibility.
count_requestedNoThe count you asked for, echoed so you never have to diff an array length against your own request.
credits_chargedNoCredits this call actually cost.
degraded_reasonNoverbosity=minimal ONLY, and only when non-null: hoisted out of `research` so the trimmed envelope still says WHY the run degraded. At standard/full read research.degraded_reason instead.
degraded_stagesNoverbosity=minimal ONLY, and only when non-empty: hoisted out of `research`; which stages to blame. At standard/full read research.degraded_stages.
grounding_refundNoNon-null ONLY when you asked for research and the VENDOR REFUSED the web-search tool: the full pipeline still ran, so you are not billed as smart, but part of the research premium is waived. {credits_waived, list_price, pct_of_research_premium, reason, explanation}. This is why credits_charged can come in UNDER base_price + price_per_hook * hooks; research.grounded_degraded_reason says what the vendor said.
score_disclaimerNoThe honest limits of the scores above. Absent at verbosity=minimal.
credits_remainingNoYour balance AFTER this charge.
estimated_secondsNoauto_job path only: queue-aware estimate of total time to a result.
poll_after_secondsNoauto_job path only: wait at least this long before the first get_job.
replayed_at_chargeNotrue when the replay was detected at the charge boundary rather than up front; either way you are billed exactly once.
llm_fallback_reasonNoWhy the fallback happened, or null.
judge_fallback_reasonNosmart/research only: why the judge did NOT rank this set, or null when it ran. Non-null means these score.totals are heuristic-scale, so never compare them against a judged run's totals.
resolved_creator_profileNoExact immutable creator-profile binding used by this occurrence. Null/absent for inline or unprofiled generation.

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the tool always fails before any model/billing work, and lists specific error responses (unauthorized; invalid_request with reason unsourced_hook_generation_archived). This is valuable behavioral context beyond schema. The 'Research requires...' sentence is slightly confusing because it describes a prerequisite for an operation that always fails, but the overall failure behavior is clearly stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with the most important fact (archived). It includes a clear alternative, failure behavior, and error codes. The sentence about research requirements is somewhat extraneous since the tool always fails, but it is only one sentence. Overall it is appropriately sized, but not as tight as the two-sentence example, so a 4.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an archived tool, the description completely covers what an agent needs: it indicates the tool is obsolete, points to the replacement, states it will fail, and lists errors. Given the high parameter count and full schema, the description's job is not to explain parameters. The output schema exists so return values are covered. The research requirement sentence adds slight ambiguity, preventing a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has comprehensive descriptions for all 28 parameters (100% coverage), so the description is not required to add parameter-level detail. The description does not reference any specific parameters except implicitly 'research' mode, but that does not raise or lower the baseline. Per the rubric, baseline is 3 with high schema coverage, and no added value is present.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Archived source-free writer,' which clearly identifies this as the archived no-source variant of hook generation, and the tool name 'generate_hooks' confirms the action. It explicitly names the replacement ('research_hook_evidence'), distinguishing it from siblings. However, it does not state a concrete present-tense action like 'generates hooks'; it labels the tool and indicates it is archived, so a 4 is appropriate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs 'Use research_hook_evidence instead' and states 'Always fails before model, template, provider, storage, or billing work,' making it unambiguous that this tool should not be used and that the sibling is the correct path. This meets the criterion for explicit when/when-not/alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, e.g., signup vs. delete_account, create_key vs. revoke_key, generate_hooks vs. score_hook. Even similar tools like generate_hooks and generate_hooks_batch are clearly differentiated by single vs. batch operation.

Naming Consistency5/5

All 32 tools use a consistent verb_noun snake_case pattern (e.g., add_credits, create_checkout, revoke_key, list_outcomes) with no mixing of camelCase or other conventions.

Tool Count4/5

32 tools is slightly above the typical 15-tool range, but the domain is broad (account, keys, webhooks, generation, scoring, jobs, outcomes), and each tool has a specific purpose. No tools seem redundant.

Completeness4/5

The tool surface covers most lifecycle operations: CRUD for accounts/keys/webhooks, generation/scoring with batch and async variants, outcomes reporting, and auxiliary tools. Missing explicit delete for hooks (expire automatically) and some update operations, but no critical gaps.

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