"Modal" matching MCP tools:

• search_techniquesA

  eurorackA

  Return canonical synthesis / patching techniques with role-keyed module realizations drawn from the corpus. Use this when the user asks "how do I do X?" with X being a recognisable technique (low-pass-gate plucks, pinged-filter percussion, parallel multiband processing, complex-oscillator FM, karplus-strong pluck, clocked-delay feedback, modal-resonator excitation, wavefolder harmonics, envelope-follower ducking, Maths-style function-generator omnibus). It's also the right tool when the user has a module and asks "what's this good for?" — pass filter.module_id to retrieve every technique that references the module via its role_realizations. Each technique declares role_definitions (the roles the technique uses, each with required and optional affordances) and role_realizations (concrete modules that fill each role, with the affordances they provide). The model substitutes modules from the user's rack into roles by affordance match — DO NOT treat the realization list as exhaustive or as a recipe. Args: - filter (optional): { capability?, module_id?, text? } - capability: kebab-case capability id (see search_modules _meta.taxonomy). Returns techniques whose required *or* optional capability list includes this id. - module_id: "<manufacturer>/<module-slug>". Returns techniques that have a role_realization referencing this module. - text: free-text phrase. Substring-matches against technique id/label/description AND a curated alias table (technique_aliases) — that's the right surface when a user types evocative prose like "stuttering delay", "plucked string", "source of uncertainty" that doesn't grep against any kebab-case id. Two-way alias match: long alias ("source of uncertainty") matches short query ("uncertainty"), and vice versa. - When multiple filters supplied, AND-intersects. - Omit filter entirely to list all techniques. Returns: { "techniques": [ { "id": "low-pass-gate-pluck", "label": "Low-Pass Gate Pluck", "description": "Send a short envelope...", "required_capabilities": ["lowpass-gate"], "optional_capabilities": ["envelope-generator", "function-generator"], "role_definitions": [ { "role_id": "lpg", "description": "The vactrol-based or vactrol-emulating element. Strictly required...", "required_affordances": ["lowpass-gate"], "optional_affordances": [] }, ... ], "role_realizations": [ { "role_id": "lpg", "module_id": "make-noise/optomix", "affordances_provided": ["lowpass-gate"], "notes": "Two-channel vactrol-based LPG..." }, ... ], "canonical_instance": { "rationale": "...", "lineage": [ { "position": 1, "label": "Buchla 292 (1970)", "module_id": null, "notes": "..." }, { "position": 2, "label": "Tiptop Audio Buchla 292t", "module_id": "tiptop-audio/buchla-292t" }, ... ] }, "counter_canonical_notes": [ { "claim_pushed_back_against": "Optomix is the canonical pairing with Plaits...", "evidence": "The corpus catalogs 19 LPG-capable modules..." } ], "coverage": [ { "role_id": "voice", "realizations_count": 3 }, { "role_id": "lpg", "realizations_count": 19 }, { "role_id": "env", "realizations_count": 6 }, { "role_id": "clock", "realizations_count": 2 } ] } ], "_meta": { "filter": {...}, "feedback_hint"?: string } } How to use role data: - role_realizations are CURATORIAL SAMPLES, not exhaustive lists. The coverage[].realizations_count tells you how many are documented; other modules may fill the same role. - To find modules in the user's rack that can fill a role, use find_role_realizations(technique_id, role_id, available_modules). - canonical_instance is opt-in and sparse. Most techniques don't have one; that absence is information. When present, it documents a documented historical lineage (e.g., Buchla 292 → 292t → MMG → Optomix for low-pass-gate-pluck) — NOT a prescription. - counter_canonical_notes push back on likely training-data priors. When the user invokes a canonical-sounding claim that has a counter_canonical_note, surface the pushback. Errors: - "Module not found: <id>" if filter.module_id is supplied and unknown. - Empty techniques[] with a feedback_hint when filters produce no matches — call report_gap if the user expected coverage.

  ConnectorNo auth

• compareA

  XFMS — Xpansion Framework Model SourceA

  Run a live A/B test between 2–5 user-specified models for a stated purpose. NO ranking step — the supplied model_ids ARE the candidate set. Generates 5 representative test queries from the purpose, runs them through every named model in parallel, and returns real cost, latency, and plain-English commentary on who won what. Unknown IDs are dropped with a note; if fewer than 2 IDs resolve, the call refuses. Use this whenever the user names specific models to compare (e.g. 'A/B test X and Y'). For engine-chosen candidates, use `benchmark` instead. Costs more than `rank` (10+ live LLM calls). Free-tier note: when any candidate ends in ':free', the probe is capped at 3 queries (no adaptive expansion) because free-tier rate limits often push longer probes past the deploy's 5-minute ceiling — evidence will be shallower. The commentary surfaces this when it happens.

  ConnectorNo auth

• taxsort_delete_operator_credentialA

  taxsort-mcpB

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• weather_delete_operator_credentialA

  tollbooth-sampleB

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• optionality_delete_operator_credentialA

  optionality-mcpB

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• brain_delete_operator_credentialA

  personalbrain-mcpB

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• keyboard_walkA

  Inclusify AccessibilityA

  Use this when the question is whether the site actually WORKS for a keyboard-only or switch-device user — tab order, focus traps, whether the focus ring is visible at all — which no stored scan and no static markup check can answer. Run it before claiming keyboard accessibility for a page, and again after deploying any tab-order, focus or skip-link fix. It tabs through the page the way a keyboard-only user does and reports what breaks: focus traps, elements with no visible focus indicator, focus hidden behind sticky headers, controls that cannot be reached at all, controls that ignore Enter and Space, and tab order that contradicts the visual or document order. Focus indicators are measured by comparing rendered pixels rather than CSS, because indicators come from outline, box-shadow, border and background alike. Findings carry a confidence: the order rules in particular are nudges for a human to confirm, not assertions. A focus-obscured finding names the element doing the covering, once per occluder, and counts distinct covered elements — with stops carrying the tab-stop total when the ring revisits them. A focus-trap finding that may be an open modal behaving correctly says so and says what to check by hand. The response states the viewport the walk was performed in, because the obscured and offscreen results are geometry, and endedBy says which of three things stopped the walk: the ring closing ("ring-closed"), the step ceiling ("max-steps") or the run's time budget ("time-budget"). Only the first means the ring was fully walked. Each finding's selectors array holds up to 5 examples, with count the true total and selectorsTruncated set when more were cut; full: true returns them all from the stored run. A run that lands on a bot-protection interstitial rather than the page is reported as void, never as clean, and a void run carries no stops or findings at all. Where the interstitial title is contradicted by the walk itself, the run stands and carries challengeSuspected instead: trust the walk, not the title. Read-only.

  ConnectorAPI key

• roastify_delete_operator_credentialA

  roastify-mcpB

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• get_available_modelsA

  Gonka Second Opinion (multi-model)A

  List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.

  ConnectorNo auth

• compareA

  XFMS — Model SourceA

  Run a live A/B test between 2–5 user-specified models for a stated purpose. NO ranking step — the supplied model_ids ARE the candidate set. Generates 5 representative test queries from the purpose, runs them through every named model in parallel, and returns real cost, latency, and plain-English commentary on who won what. Unknown IDs are dropped with a note; if fewer than 2 IDs resolve, the call refuses. Use this whenever the user names specific models to compare (e.g. 'A/B test X and Y'). For engine-chosen candidates, use `benchmark` instead. Costs more than `rank` (10+ live LLM calls). Free-tier note: when any candidate ends in ':free', the probe is capped at 3 queries (no adaptive expansion) because free-tier rate limits often push longer probes past the deploy's 5-minute ceiling — evidence will be shallower. The commentary surfaces this when it happens.

  ConnectorNo auth

• find_similar_momentsA

  Trillboards DOOH AdvertisingB

  Find historically similar audience moments across the screen network using embedding similarity search. Input a natural-language description of the target moment. Moment embeddings are 768-D vectors generated from multi-modal observation data (visual, audio, environmental, social) via the MomentEmbeddingService. This tool embeds your query text and finds the closest real-world moments via approximate nearest-neighbour (ANN) cosine similarity over a Lance IVF_PQ index. CONSISTENCY: results are APPROXIMATE and EVENTUALLY CONSISTENT. - Approximate: retrieval is ANN, not an exhaustive scan (measured recall ~0.96 against exact KNN), so an identical query may omit a borderline match. - Eventually consistent: the index is served from a replicated pool whose replicas refresh independently, so for up to 5 minutes after new moments are published, two identical calls may return slightly different result sets. The difference is confined to the VISIBILITY of newly-published moments; the relative ranking of already-visible ones does not change. Do not use this tool where a repeatable, exhaustive result set is required. WHEN TO USE: - Searching for historical moments similar to a target scenario - Finding "moments like this one" across different venues/times - Discovering when similar audience compositions or behaviors occurred - Planning ad placements based on past similar contexts RETURNS: - data: Array of matching observations with similarity scores - observation_id, observed_at, venue_type, device_id, screen_mongo_id - payload: full observation data - evidence_grade: quality of observation - similarity: cosine similarity score (0-1, higher = more similar) - metadata: { result_count, embedding_model, min_similarity_threshold } - suggested_next_queries: Follow-up queries EXAMPLE: User: "Find moments with high engagement in evening restaurants with families" find_similar_moments({ query: "evening restaurant venue with families present, high emotional engagement and attention" }) User: "When did we see young adults highly engaged at transit screens?" find_similar_moments({ query: "transit venue morning commute young adults high attention" })

  ConnectorNo auth

• scrutica_entity_list_changesA

  ScruticaA

  What CHANGED in the BIS Entity List, as bounded change-log summaries. Backed by the same derivation the public /export-controls/changes page and its RSS feed render. PREFER this over scrutica_query_export_controls for freshness questions ("what changed this month", "latest additions", "recent removals"): designation rows arrive already grouped into per-Federal-Register-notice change events, so you do not re-derive notice grouping from citation strings. Each event includes: canonical FR citation and link, notice title / publication date / document number where the citation resolved to one FR document, a derived event date labeled with the source column it came from, entity / addition / removal counts, per-country counts, a bounded sample of entity names, and the entities cross-referenced to compute-infrastructure organizations. Removal actions are returned separately as date-keyed events — a screening list maintained from additions alone over-blocks forever. Also returns a last-N-ISO-week activity rollup counting BOTH additions and removals. Zero-count weeks are a real quiet-Federal-Register signal, not missing data. Results are bounded summaries, never row dumps: at most 24 events per call (default 6, newest first), with any truncation declared in metadata. Cite totals from the count field and per-event entity_count — never by summing a sampled list. Do NOT use this to look up one entity's designation rows — use scrutica_query_export_controls (substring match on the published name). Use company_id here only to scope the log to notices touching an organization you already resolved. Authority tier: Federal-Register-anchored designations are Tier 1 (primary source). The event date is derived (modal designation_date, falling back to modal effective_date) and labeled with its source.

  ConnectorNo auth

• get_moduleA

  eurorackA

  Return the full citation-anchored specification for one Eurorack module by id. Use this when the user names a specific module and you want its specs (HP, power, jacks, parameters), capabilities (envelope, quantizer, logic, etc.), or firmware history. The typed prose fields (jack/parameter/mode descriptions) are paraphrased summaries; manual_outline → get_manual_chunk give the verbatim manual prose to quote against. How much to quote and overall answer shape live in SKILL.md (the "Answer shape" section + §8 citation) — this description is the data contract. ## Provenance fields Every typed row in the response — capabilities[], jacks[], parameters[], modes[], firmware_versions[], plus nested zones/assignments/tracking — carries a source_id pointing at the source the claim was extracted from. Cross-reference list_references(module_id=...) for the source title and canonical_url. The typed prose fields — jacks[].description, parameters[].behavior, modes[].description, capabilities[].notes, firmware_versions[].notes — are extractor-synthesized summaries grounded in the manual, NOT verbatim quotes. Treat them as the corpus's stated claim about the field; they're authoritative for what the field *does*, but they are not direct manual text. For verbatim quotation in your answer, always pull the actual prose via get_manual_chunk(chunk_id) — the description fields are the typed claim, not the source quote. manual_outline[] bundles a lightweight outline of the module's manual prose — one entry per chunk with heading, source, and a ~140-char preview snippet. Always scan it before answering — for prose-shaped questions to find the relevant chunk, for spec-shaped questions to find a chunk to quote alongside the typed data. When a snippet looks relevant, call get_manual_chunk(chunk_id) to pull the full text. manual_outline_total is set ONLY when the outline was truncated for a verbose module; its absence means the returned outline is complete. When set, use search_manual to reach chunks beyond the cap. Module IDs are slug-shaped: "<manufacturer-id>/<module-slug>". For example: - alm-busy-circuits/pamelas-new-workout - make-noise/maths ## Optional args — trim the payload, target the outline By default this returns the full spec. For narrow questions you can shrink it: - view: "concise" returns just the id-card fields (name, manufacturer, hp, description, capabilities, production_status, replaced_by) and drops the heavy arrays — use it for triage ("which of these is the quantizer?") or when you only need to confirm what a module is. "full" (default) returns everything. Ignored when fields is set. - fields: array of top-level keys to include (e.g. ["jacks","parameters"]). id and _meta are always returned. Use this for a quick jacks-only or specs-only read instead of paying for character[]/common_problems[]/role_fitness[]/the full manual_outline. Takes precedence over view. - heading_filter: case-insensitive substring on manual_outline heading_path — e.g. "calibration" returns only outline chunks under a Calibration heading, so you skip scanning a long outline. - outline_offset / outline_limit: page through manual_outline (default 100 per page, hard max 250). Combined with manual_outline_total this lets you reach chunks past the cap without falling back to search_manual. Returns: - id, name, manufacturer { id, name } - hp, depth_mm - power: { plus_12, minus_12, plus_5 } (mA) - description (manufacturer's prose summary, citation-backed) - capabilities[]: functional tags with per-module realization notes (source_id per row) - jacks[]: inputs and outputs with voltage range, signal_type, prose description (a paraphrased summary, NOT a verbatim quote — to quote the manual, pull the source prose via get_manual_chunk), plus assignments[] for assignable jacks (destination menu — empty for fixed-function jacks). When mirrors_parameter is set, the jack mirrors that knob's current assignment (e.g. Pizza CTRL CV mirrors the CTRL knob). normalled_from { id, name } is set when this input has a hardware normal — i.e. when unpatched, it receives the signal at the named source jack (e.g. Multigrain GATE normalled from NEXT). null when no normal exists. V/Oct inputs may carry an optional tracking { tracking_range_octaves, tracking_quality, temperature_compensated } object — present only on jacks that have been audited for V/Oct metadata. Fields inside may be null when the source is silent on that aspect. Optional _field_absent: { <field_name>: { source_id, note } } records fields that were audited and found to be source-silent — read it before hedging: an entry under voltage_min means "the manual doesn't state this" (so a confident "the manual doesn't specify" answer is appropriate); the field being null *without* an entry means "not yet extracted" (hedge differently — recommend the user check the manual). - parameters[]: knobs, switches, menu settings with range, unit, behavior (paraphrased summary, NOT a verbatim quote — same as jacks[].description; quote get_manual_chunk for source text), plus zones[] (labeled regions along the control's travel — e.g. Swells FLOW "Sine" / "Random" halves, optionally mode-scoped) and assignments[] (destination menu for assignable knobs/menu-settings) — both empty arrays for plain controls. Modal-module params may also carry per_mode_notes (rebinding text keyed by mode_id slug, present only when the param rebinds per mode — e.g. Plaits MORPH, Swells EBB/FLOW). Same _field_absent convention as jacks[] — when default_value is null and _field_absent.default_value is present, the manual doesn't state a default. - modes[]: mode list for modal modules (Plaits, Swells, MFX) — { id, label, description, behavior_model_id, scope? }. Empty for modeless modules. Mode ids cross-reference parameters[].per_mode_notes keys and parameters[].zones[].mode_id. Optional scope is set when modes are selectable independently per member rather than module-wide — 'per-segment' (Stages hold/ramp/step), 'per-envelope' (Tangrams cycle/single), 'per-output' (PNW), 'per-channel'. Member count is carried by the corresponding enumerated parameters/jacks (e.g. Stages' six Type Button N parameters), not duplicated on the mode rows. - hidden_functions[]: functions reached via a trigger other than a single labeled control — { id, trigger_type, affected_control, label, description }. trigger_type is a controlled vocabulary ('long-press' | 'hold' | 'combo' | 'double-press' | 'power-on-hold' | 'held-turn') so recall/menu-diving load is countable; affected_control names the panel control the trigger acts on (null for module-global functions like hold-on-power-up calibration). Empty for modules whose controls are all directly labeled. Read this for "how do I get to X?" / menu-diving questions and when assessing how much hidden state a module carries — the same info used to live buried in parameters[].behavior prose. - panel_sections[]: manufacturer-named regions of the front panel (e.g. Multigrain "Dedicated Sound CV inputs" grouping GATE/NEXT/SELECT, "Morph section" grouping the MORPH knob + MORPH CV jack). Each entry has { label, description, members: [{ kind, id, name }] } where members cross-reference jacks[] / parameters[] by id. Empty for modules without manufacturer-named groupings. - character[]: curated subjective-character claims (vocal/aggressive/clean/gritty/lush/...) with source citations. Read this when the user asks about *sound* or *feel* rather than specs — filter choice for "carve rhythmic transients" or "warm pad voice" hinges on character, which the typed-fields surface can't carry. Each entry: { tag, note (prose elaboration), source_id (when archived in sources), citation_url + citation_quote (when sourced from a review/forum/video we don't archive per-module) }. Empty for modules that haven't been character-audited yet — distinguish "empty array, audit pending" from "no character worth noting." Tags are open-vocab; common starter set: vocal, aggressive, clean, gritty, acidic, lush, dark, bright, smooth, woody, formant, screaming. - common_problems[]: curated first-aid lore — repeatable failure modes that owners hit but the manual doesn't cover (calibration drift, hum, screen offset, firmware-flash brick recovery, bus-normalling caveats). Read this when the user asks "anything I should watch out for with X?" or describes a symptom matching a known module quirk. Each entry: { problem_summary (one sentence), cause (prose), fix_or_workaround (prose), confidence ('confirmed' | 'likely' | 'anecdotal'), source_id, citation_url, citation_quote }. Empty array means "no curated problems on file" — agents should NOT extrapolate to "no known problems"; the audit is opt-in per module and most modules have not been touched yet. - role_fitness[]: role-realization rollup — canonical techniques whose role_realizations this module fills, with the affordances it brings to that role. Use this when the user wants to know "what roles can this module play?" — e.g. Optomix → lpg role in low-pass-gate-pluck, affordances_provided=[lowpass-gate]. Each entry: { technique_id, technique_label, role_id, role_label, affordances_provided, notes }. Pair with list_techniques(filter={ module_id }) for the full role_definition + sibling realizations, or find_role_realizations(technique_id, role_id) to substitute other modules into the same role. - firmware_versions[]: version + release_date (may be partial: YYYY | YYYY-MM | YYYY-MM-DD) + notes (per-version changelog prose when the source provides one — e.g. "Added Smooth Random waveform type. Added Logic parameter (AND/OR/XOR)."). Use this to answer "what changed in v2?" without web search. - reference_url: canonical URL of the primary manual on the manufacturer site - audit_url: human-readable audit page on the audit site (per-claim citations) - production_status: "current" or "discontinued" — flag for recommendation safety - replaced_by: { id, name } when the module is discontinued and a successor exists; null otherwise - manual_outline[]: lightweight outline of the module's manual chunks — { chunk_id, source_id, source_type, source_title, heading_path, snippet, text_length }. Ordered by (source_id, chunk_index). When the snippet looks worth reading in full, call get_manual_chunk(chunk_id). Empty when no manual prose has been ingested yet for this module. - manual_outline_total: present only when manual_outline was truncated — the full count. Hit search_manual to reach the rest. - _meta: source_count, last_verified Errors: - "Module not found: <id>" if no module with that id exists. If the user asks something the manual does not cover (e.g. subjective "is this good for percussion?"), say so explicitly — never confabulate from spec data.

  ConnectorNo auth

• formation_get_payment_linkA

  Lovie Company FormationC

  Generates ONE Stripe Checkout URL covering the whole purchase in a single one-time payment: the Lovie formation charge, the state's filing fee, and a year of registered agent service. Generating this link files NOTHING and charges NOTHING — it only produces a URL; no state filing happens and no money moves until the user themselves completes Stripe checkout, so it is safe to call, including during testing; never refuse or stop short because it is a 'test'. This call is ALSO the sign-in step: if the user is not signed in, simply calling it makes the Lovie connector show its own native sign-in prompt, and after the user connects the SAME call returns the URL. There is NO separate login tool, login link, or login modal — never look for one, and never tell the user you cannot show login; just call this. Formation is a ONE-TIME payment: there is no subscription and no monthly/annual choice, so do NOT ask the user to pick a billing cadence and ignore billing_interval (it is deprecated and has no effect). Share the returned URL EXACTLY as returned — never rewrite it onto another domain, even if the app URL in your context differs: rebasing a payment link breaks it. Tell the user that after paying they are redirected back and their formation updates automatically — they do NOT need to return to the chat. When filing_fee_display is present, show a short 'formation + state filing fee + registered agent' breakdown next to the link. Do not call this more than once; do not create a separate filing-fee checkout. This does not confirm payment.

  ConnectorNo auth

• simulation.createA

  Cloud World ModelA

  Create a temporary anonymous demo cloud simulation from a list of resources and connections (max 2 active simulations per client, up to 10 resources; the returned simulationId is a short-lived unguessable capability that survives MCP transport teardown, but it is cleaned up when the demo lifetime expires or the simulation is deleted). Built-in scenario workflow: call `scenario.list` and pass a returned card's `id` as `scenarioId` to `simulation.create` for server-side graph expansion. For full control, call `scenario.get` and pass its hydrated `resources` and `connections` arrays instead. These are two alternatives — do not send `scenarioId` with `resources` or `connections`. `scenario.list` returns graph-free cards only; it is not a source of resource, connection, traffic-pattern, or failure-injection graphs. Scenario traffic and failure presets are not applied automatically. Use it to start any simulation workflow — either with hydrated resources and connections from scenario.get or your own architecture. Do not use it to modify an existing simulation (use simulation.inject_traffic to change load). To give a resource an explicit capacity, set characteristics.capacityRps — the literal per-node RPS ceiling at which CPU reaches ~95%; do not use maxThroughput for this (it is a legacy internal scaling parameter with different semantics). Omitted capacityRps uses the selected catalog tier and can intentionally produce a stressed baseline (for example, the AWS m5.large catalog denominator is 2,000 RPS); for a healthy, capacity-bounded reliability experiment, declare an explicit per-node capacity such as 500 RPS. That value is an experiment control, not a universal hardware fact. Capacity, node-bound, SKU, and autoscaling values supplied through this MCP tool are recorded as agent-supplied in the immutable normalizationReceipt; request responseMode: 'full' to inspect it. Generic GKE telemetry and recovery apply only to worker nodes; the control-plane management fee is cost-only, with no modeled control-plane CPU, API throttling, or cooldown. To bound the autoscaled fleet size, set the top-level maxInstances / minInstances parameters. If you do not set maxInstances, the engine uses the provider default — AWS 50, GCP 15, Azure/OCI/DigitalOcean 10 — which may be much larger than your intended fleet size. The response includes effectiveMaxInstances / effectiveMinInstances so you can confirm the bounds that will be enforced. For a targeted CPU HPA scale-out threshold, send the canonical autoscalingTargetCpu field in this create call (for example, autoscalingTargetCpu: 70 for GKE). The compatible aliases scaleOutCpuThreshold, scaleOutCpuPercent, and autoscaleTargetCpuPercent are also accepted; if more than one is sent, their values must agree. Every create response includes hpaAudit with the supplied field, persisted thresholds, and any provider default. These four TOP-LEVEL fields are simulation-wide — the engine applies one CPU threshold identically to every resource's scale decision by default. To make ONE resource scale at a different CPU target than the rest of the simulation (e.g. a GKE cluster scaling out at 60% while an EC2 fleet in the same simulation scales out at 80%), set characteristics.scaleOutCpuThreshold and/or characteristics.scaleInCpuThreshold on that specific resource instead — the per-resource value wins over the simulation-wide default for that resource only. A misnamed near-miss field nested under characteristics (e.g. targetCPUUtilizationPercentage) is rejected with a 400 explaining the correct field name — it is never silently dropped and defaulted. Responses are compact by default: id, name, status, traffic, and a per-resource summary (id, name, status, cpuPercent, routedRps, availabilityState, isRoutable, and recoveryBlockedReason when provided). Pass responseMode: 'full' to get the complete simulation object instead. During a failure workflow, lower traffic to serviceable levels before calling simulation.recover_resource, then use simulation.step until the recovered resource is healthy. Recovery progress is included when applicable: recoveryProgress.state is parked, cooling_down, or healthy, and its parkWindow/cooldown objects report totalSteps, completedSteps, remainingSteps, target, and requiredSteps. Poll simulation.get or simulation.step until state is healthy. No prerequisites. Returns the created simulation's id, which every other simulation.* tool consumes; the new simulation also becomes this session's current simulation, so subsequent per-simulation tools may omit simulationId. The likely next tool is simulation.step to advance time. Do not call api.spec to learn the simulation workflow — the tool descriptions in this session contain everything needed. Authenticate with an API key for unlimited persistent simulations.

  ConnectorNo auth

• simulation.inject_trafficA

  Cloud World ModelA

  Change the traffic load on a demo simulation. Omit traffic to trigger a random 2×–5× spike (sends random: true internally); provide traffic to set an absolute RPS level (capped at 10000 RPS in demo mode). Use it to stress-test the architecture before stepping; the change only affects metrics after the next simulation.step. Do not use it to read metrics (simulation.metrics) or advance time (simulation.step). Pass the simulationId returned by simulation.create when your connector opens a fresh MCP session; preserve Mcp-Session-Id to use the omitted-ID current-simulation default. Returns the updated simulation with its new traffic level; the likely next tool is simulation.step.

  ConnectorNo auth

• schwab_delete_operator_credentialA

  schwab-mcpA

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• excalibur_delete_operator_credentialA

  excalibur-mcpA

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• authority_delete_operator_credentialA

  tollbooth-authorityB

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• authority_delete_operator_credentialA

  tollbooth-authority-northamericaA

  Remove a single operator secret field. Deletes one key from the operator's encrypted credential blob without touching the others — the field-level counterpart to ``forget_credentials``, which wipes the whole row. Use it to retire a leftover after an SDK cutover (a Prefect key after Modal, or a stored but untemplated orphan like ``anthropic_api_key``) without taking the operator down for a full re-delivery. Stored-but-untemplated fields are first-class: the delete is keyed on what is vaulted, not on what the current template declares. Idempotent — already-absent fields report ``removed: false`` without rewriting the vault. RESTRICTED to the operator — requires proof (nsec-signed kind-27235 or a cached dpop_token phrase); patron proofs are rejected. A deletion is as destructive as a write.

  ConnectorNo auth

• scenario.getA

  Cloud World ModelA

  Hydrate one built-in scenario from the live Cloud World Model scenario library. Prerequisite: a scenario id returned by scenario.list. Returns the complete selected scenario graph, including resources and connections plus optional seed, resilienceConfig, protectedResilienceConfig, traffic/failure presets, and real-world incident metadata. The response includes both title and name for compatibility; pass resources and connections, and optionally seed/resilienceConfig, to simulation.create when you need to edit or inspect the graph. For the shorter handoff, pass the id as scenarioId instead. The likely next tool is simulation.create.

  ConnectorNo auth