"Importing FastMCP from the mcp.server Python Module" matching MCP tools:

• acc_search_documents

  ScanBIM MCP

  Full-text search the ACC Docs module on a project for drawings, specs, submittals, and other documents matching a query string. Calls the APS Data Management v1 search endpoint scoped to a project. When to use: an agent needs to locate a spec section, a sheet, or a submittal by keyword (e.g. 'fireproofing', 'A-101', 'RFI 23'). When NOT to use: you already have the document URN/lineage — fetch it directly. You want the file contents — this returns metadata; download separately via Data Management. APS scopes: data:read account:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied (Docs module access required); 404 project_id not found — check the ID (note: this endpoint re-prepends 'b.' so pass the UUID form); 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: READ-ONLY. Inserts a row into D1 usage_log. Idempotent.

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• reachable_pairings

  eurorack

  Given a rack (a set of module ids the user owns), rank the modules NOT in the rack by how many rack members they pair with. The set-level companion to find_compatible_with: where that answers "what pairs with module X?", this answers "given my whole rack, what single module should I add — the one that pairs with the most of what I already have?". The ranking signal is `pair_count` — the number of DISTINCT rack members a candidate pairs with. A module that modulates five of your modules ranks above one that modulates one. This aggregate is the point: you can't get it from per-module find_compatible_with calls without tallying distinct members by hand. Use this for: - "What should I add to a rack with <modules>?" / "what fills out this system?" - "Given these modules, what pairs well with the most of them?" - Inspecting a rack's own internal pairing structure (the `internal` edges). Combination edges only. Ranking uses the seven patch-time relationships (clock-source-for, cv-source-for, modulator-for, audio-source-for, quantizer-for, trigger-source-for, envelope-target-for) — the "A and B work together in a patch" kinds. The substitution/catalog kinds (alternative-to, replaces, expander-for) are deliberately excluded: a pairing recommender shouldn't suggest replacing your modules with each other. For "what's an alternative to X?" use find_compatible_with. Args: - rack (string[], required): module ids, e.g. ["make-noise/maths", "mutable-instruments/plaits"]. Ids that match no module are returned in `unknown_ids` (and in `unresolved` with did-you-mean suggestions) rather than failing the call. Surface those rather than proceeding on a partial rack: the server is stateless about your rack — it keeps no memory of it between calls, so pass the COMPLETE current set every call. Max 64. - relationship (string, optional): restrict ranking to one combination kind above. Omit to consider all seven. - limit (number): default 25, max 100. Returns: { "rack": [{ id, name }], // the rack members that resolved "unknown_ids": [string], // rack ids that matched no module "internal": [{ from_module_id, to_module_id, relationship, source_id }], // edges within the rack "candidates": [{ id, name, manufacturer, pair_count, "pairings": [{ rack_member, relationship, direction, source_id }] // why it pairs, per member }] } `direction` on each pairing is relative to the rack member: 'outbound' = the candidate is the role-bearer (it `relationship`s the member, e.g. the candidate is a modulator-for the member); 'inbound' = the member is the role-bearer. Coverage caveat: rankings are only as dense as module_relationships. A thin or empty result means the corpus hasn't recorded those edges yet, not that no good pairing exists — call report_gap if you expected matches.

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• find_compatible_with

  eurorack

  Return modules that have a typed compatibility relationship with the given module. Both edge directions are returned and tagged via the per-match `direction` field — so a single call answers both "what is X a R for?" and "what is a R for X?". `relationship` is OPTIONAL. Omit it to get EVERY edge touching the module across all relationship kinds — the bare "what pairs with / relates to X?" question — with each match self-describing via its own `relationship`. Pass a relationship to restrict to that one kind. Prefer the relationship-less call when you don't already know which kind exists; reach for the typed form only when the question names a specific role ("what clocks X?"). Use this for two question shapes: 1. Patch-time compatibility — "what could I use as a clock source for X?" (returns matches with direction='inbound'), or "what does X clock?" (direction='outbound'). 2. Catalog comparison — "what's an alternative to X?" (symmetric), "what does X replace?" (outbound) / "what replaces X?" (inbound), "is there an expander for X?" (inbound). The vocabulary describes the edge as stored (from = role-bearer, to = target): Patch-time: - clock-source-for — A clocks B - cv-source-for — A produces CV that B consumes - modulator-for — A is a modulator suitable for B (LFO, S&H, random) - audio-source-for — A is an audio source for B (typically a VCO into a VCF) - quantizer-for — A quantizes for B - trigger-source-for — A produces triggers that B consumes - envelope-target-for — A is something B's envelope output is designed to drive Catalog: - replaces — A is the newer successor to B (Morphagene replaces Phonogene) - alternative-to — symmetric: A and B occupy similar design space with different character - expander-for — A is an expander module for the host module B Direction tag on each match: - outbound: queried module is the FROM side (role-bearer). Match is what the queried module does as R. - inbound: queried module is the TO side. Match is the R-for the queried module. - symmetric: only for alternative-to. Args: - module_id (string, required): "<manufacturer-slug>/<module-slug>" - relationship (string, optional): one of the values above. Omit for all edges. - limit (number): default 50, max 200 Returns: { "module": { id, name }, "relationship": <relationship> | null, // null when none was passed (all-edges query) "matches": [{ id, name, manufacturer, notes, source_id, direction, relationship }] } If the module is unknown, returns an error. If no relationships have been recorded in either direction, returns matches=[]. The `notes` field describes the edge in the canonical A→B direction; combined with `direction` the caller can read it correctly either way.

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• architect.certify

  AI Design Blueprint

  Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. **RECOVERY FIRST**: the run_id is emitted in the FIRST notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently within a 20-minute budget. This is the canonical recovery path. Use it before considering any retry. Long-running LLM call (60-180s typical; exceeds Claude Code's ~60s idle budget); MCP clients commonly close the call before the server returns. Retrying re-runs the LLM call AND burns one of your 3 cert retry-budget attempts. Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. MANDATORY DOCTRINE RULE (load-bearing): the badge certifies the EXACT code that produced the validate run_id, NOT 'this codebase' in general. If you modify, fix, or iterate the code between architect.validate and architect.certify — even a single character — cert rejects with code_fingerprint_mismatch. Fixing the code voids the run. The recovery path is always: edit code → architect.validate → fresh run_id → architect.certify on the fresh run. Do NOT cert from a stale run_id after iteration; ask the user to re-validate first. WHEN TO CALL: only after architect.validate returned tier=production_ready AND the user wants the certified badge AND the code has not been touched since the validate run. NOT for tier=draft/emerging/not_applicable runs (typed rejections fire — see below). NOT idempotent across attempts: each call is one of the 3 attempts in the retry budget. BEHAVIOR: atomic one-shot single LLM call, ~60-180s server-side at high reasoning effort (small payloads finish faster; observed p99 ~250s; server-side budget is 20 min, ~5× observed max). Exceeds typical MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST notifications/progress event fires at t=0 carrying the run_id. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. Updates the persisted run's result_json (public review URL + me.validation_history(run_id=...) reflect the cert outcome). ELIGIBILITY GATE (typed rejection enum on failure): caller must own the run, tier=production_ready, less than 24h old, not already certified, within cert retry budget (max 3 attempts), no other cert call in flight for the same run_id, code fingerprint must match the validated code, AND the submitted payload must be cert-payload-complete (see Payload Completeness below — cert rejects pre-LLM with `payload_incomplete` when an imported module's surface isn't visible in the validate payload that produced this run_id). Rejection reasons (typed Literal): auth_required, paid_plan_required, run_not_found, not_run_owner, not_eligible_tier, not_agentic_component (tier=not_applicable runs), already_certified, certification_age_exceeded, retry_budget_exhausted, code_fingerprint_mismatch, code_fingerprint_missing, code_not_on_file (caller omitted `code` argument AND the 24h cert-retry hold for this run has expired or was never written. Recovery: re-run architect.certify from the same MCP session that ran architect.validate, passing the code explicitly — the server never persists code by design), payload_incomplete (submitted/validated payload imports modules whose contents aren't visible — cert refuses pre-LLM to prevent a false-precision downgrade. Recovery: re-validate with verbatim public-surface stubs for every imported module, then re-cert on the fresh run_id. Empirically validated: PR #157 iter8/iter9 cert rejections were exactly this class — code on disk was correct, the submitted payload merely omitted module visibility), cert_consensus_score_below_threshold (consensus_median<75 — consensus runs only), cert_consensus_unstable_blocker (any principle mode_stability<80% — consensus runs only), run_state_corrupt, cert_persistence_failed, cert_in_flight (a prior architect.certify call on this run_id is still running. Poll me.validation_history for the verdict; do not retry until it resolves). PAYLOAD COMPLETENESS (load-bearing for cert eligibility): the cert reviewer reads the EXACT payload that produced the validate run_id. Imported modules whose surface isn't present in the payload cause pre-LLM `payload_incomplete` refusal. Avoidance — when validating with intent to cert, bundle public-surface stubs for every imported module: `from sqlalchemy.exc import SQLAlchemyError` → include a stub class; `from app.db import models` → include a `class models:` namespace stub with the columns/methods you reference; module-level imports of `dataclass`, `Literal`, `json`, `datetime`, `timezone` MUST also be in the payload (cert correctly catches when they're omitted — code would NameError on import). 'Submit Like Production': the payload should be the code as it would actually run, not a compressed sketch. The stubs cover IMPORTED dependencies only; the certified code's own enforcement branches (approval gates, policy checks, recovery paths) must be present in full. A `# ...` placeholder reads as an ABSENT control and is graded against you, not as shorthand for one that exists. PRE-LLM REJECTION AUDIT TRAIL: when cert rejects before the LLM call (payload_incomplete, code_fingerprint_mismatch, etc.), `certification_attempts=[]` on the response — no attempt landed in the retry budget, no LLM hop occurred. The rejection envelope's `rejection_reason` + `guidance` are the actionable surface. (Audit-trail UI surfacing of pre-LLM rejections is tracked in the platform self-audit set as anomaly #5; out of scope for the cert tool itself.) INPUTS: re-send the SAME code that produced the run_id (the architect persists findings + recommendations, never code, by design — privacy-preserving). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: Bearer <token>, Pro or Teams plan required. UK/EU data residency (Cloud Run europe-west2). Code processed transiently by OpenAI (no-training-on-API-data) and dropped; payloads JSON-escaped + delimited as inert untrusted data — prompt-injection inside code is ignored. If the cert call fails outright (provider error, persistence error), a fresh architect.certify is the recovery path; the eligibility gate enforces the 3-attempt retry budget. For long-running cert workflows the answer is to re-validate, not to make this tool stateful. OUTCOMES: certification_status ∈ {confirmed_production_ready (badge mints), downgraded_to_emerging (cert review surfaced a missed production_blocker, tier capped at C/emerging), unavailable_provider_error (LLM call failed, retry within budget)}. Cert findings + summary + attempt history surfaced on the persisted run for full inspectability.

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• ateam_redeploy

  ateam-mcp

  Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).

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• resolve_modules

  eurorack

  Bulk fuzzy name → id resolver. Pass a list of module NAMES (the way a user writes their rack — "Maths", "Plaits", "Pamela's New Workout", "Morph 4") and get back, per name, the best-matching corpus module id plus recovery affordances. This is the "import my rack" tool: resolving a 60-module inventory is ONE call here, not 60 search_modules calls. Use this the moment a user gives you a list of module names to map onto the corpus — before get_modules / draw_patch_diagram / reachable_techniques / rack_redundancy, all of which want ids. Feed the resolved best_match.id into those. For each input name it runs the same resolution search_modules uses (exact id, unique-slug recovery, module_aliases, separator-fold, token-AND, and edit-distance + multi-token fuzzy "did you mean"), then returns: - best_match: the single resolved module ({ id, name, manufacturer, hp, capabilities, production_status }), or null when nothing resolved confidently. - score: confidence 0..1 (1.0 exact id, ~0.97 unique slug / alias, ~0.9 literal name hit, ~0.72 token-AND relaxed, 0 when best_match is null). - match_kind: "id" | "slug" | "exact" | "relaxed" | "ambiguous" | "fuzzy" | "none". - did_you_mean: up to 5 fuzzy neighbours to confirm — populated when best_match is null (a near-miss like "morf 4" → joranalogue/morph-4, or "tiny time machin" → oam/tiny-time-machine). - ambiguous: up to 5 candidates when several modules tied for the match and the server declined to pick (e.g. a slug shared across manufacturers). Present them to the user; do NOT silently take the first. The server AUTO-RESOLVES a confident single match but never guesses through a genuine tie — that's the ambiguous bucket. A name with best_match=null AND empty did_you_mean/ambiguous is a real corpus gap: consider report_gap(kind="missing_module"). Args: - names (string[], required): module names or ids, up to 100. Order and duplicates are preserved in the response. Returns: { "resolutions": [ { "input": "Pamela's New Workout", "best_match": { "id": "alm-busy-circuits/pamelas-new-workout", "name": "Pamela's NEW Workout", "manufacturer": "ALM Busy Circuits", "hp": 8, "capabilities": [...], "production_status": "current" }, "score": 0.9, "match_kind": "exact", "did_you_mean": [], "ambiguous": [] }, ... ], "_meta": { "requested": 3, "resolved": 2, "feedback_hint"?: "..." } } Errors: - Throws only on a malformed call (missing/empty names array, or > 100 names). Unresolved individual names are NOT errors — they come back with best_match=null and the recovery fields.

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• acc_list_rfis

  ScanBIM MCP

  List up to 50 RFIs from an ACC project, optionally filtered by status. Returns a normalized array of {id, subject, status}. When to use: you need a quick rollup of outstanding or answered RFIs on a project, or to find a specific RFI id. When NOT to use: you want the full response thread of a single RFI — use the ACC UI or per-RFI endpoint. This tool caps at 50 results and does no pagination. APS scopes: data:read account:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied (RFIs module may not be enabled on the project); 404 project_id not found — check the ID; 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: READ-ONLY. Inserts a row into D1 usage_log. Idempotent.

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• reachable_techniques

  eurorack

  Given a rack (the module ids the user owns), return which canonical patch techniques the rack can realize, and which it is one module away from. The set-level companion to find_role_realizations: where that answers "which module fills role R in technique T?", this answers the rack owner's actual question — "given everything I own, what can I actually do, and what am I close to?". This is the right tool the moment a user gives you their modules and asks an open "what can I do / what can this rack do / what am I missing?" question — instead of guessing techniques from training priors or calling find_role_realizations technique-by-technique by hand. It runs the affordance match across the whole technique catalog for you. Returns two buckets: - reachable: every required role has a rack module that fills it. Each carries an `assignment` (role → module). `requires_shared_module: true` flags a technique only reachable by reusing one module for two roles — verify those roles can share one instance. - near_misses: all-but-one role fillable; `missing_roles` names the unfilled role(s) and the `required_affordances` you'd need. This is the acquisition signal — "you can already do X; you're one <affordance> module away from Y". Args: - rack (string[], required): module ids, e.g. ["make-noise/maths", "mutable-instruments/plaits"]. Max 64. Ids that match no module are returned in `unresolved` (with did-you-mean), not silently dropped. - limit (number): max techniques per bucket. Default 25, max 100. Stateless-rack contract: the server keeps no memory of your rack between calls — pass the COMPLETE current rack every call. A partial rack silently narrows what's reported reachable, so if a module id doesn't resolve, surface the `unresolved` did-you-mean to the user rather than proceeding on the incomplete set. Scope: reachability is role-PRESENCE based. It does NOT verify per-role instance counts (cardinality) — a technique needing two independent envelopes is judged reachable if you have one envelope source. The distinct-instance question (can one module fill two roles?) is surfaced as `requires_shared_module`, not silently assumed. For the editorial detail on a specific technique (canonical instance, counter-canonical notes, full realization list), call list_techniques; for one role's candidates, find_role_realizations. To go the other way — which of your modules are redundant / safe to sell — call rack_redundancy.

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• check_injection

  ContrastAPI

  Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.

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• draw_patch_diagram

  eurorack

  Patch diagram tool. Use when the user describes routing across multiple Eurorack corpus modules. Renders modules as boxes laid out by wire topology (matrix-shaped patches anchor on a hub; otherwise modules step left-to-right by signal-flow rank), jacks as colored ports keyed to signal type, wires as bezier curves. Inline SVG on claude.ai surfaces (web, Desktop chat, mobile); JSON elsewhere. (When to *offer* a diagram unprompted: SKILL.md §4.) **Trigger phrases:** "show me the patch", "draw what I just described", "remind me what's connected to what", "explain the routing", or any time you'd otherwise hand-draw a patch in SVG/text — use this instead of drawing. Strict gate — call only when ALL of: 1. At least 3 named corpus modules. 2. Explicit wire connections between them (user-stated or derived from a coherent description). 3. The patch is concrete — user is following a tutorial, describing their own rack, or referencing back what's connected to what. Do NOT call for: a single module, a question about one module's jacks, "what should I patch X to?" (that's a recommendation, not a graph), or hypothetical patches with unnamed placeholders ("connect a VCO to a filter"). Jack names. Corpus jack names are descriptive ("V/Oct CV input", "TRIG input", "Strumming trigger input"), not panel-text shorthand ("V/OCT", "TRIG"). The resolver accepts panel-text as a fallback when it unambiguously substring-matches one jack of the right direction (e.g. "TRIG" → "TRIG input"); successful resolutions surface as `panel_text_resolved` warnings so you can confirm. Ambiguous panel text ("OUT" on a multi-output module) errors with the candidate list. To skip the fallback entirely, call get_modules to discover the exact corpus names up front (one round trip for the whole batch). Multi-channel modules require a CH<N> prefix. Modules with per-channel jacks (Quadrax, Maths, Tangrams, Stages, Optomix, QMMG, DXG, Pamela's New Workout, Cold Mac, etc.) enumerate each channel separately — e.g. `CH1 TRIG`, `CH2 TRIG`, `CH3 TRIG`, `CH4 TRIG` on Quadrax. Bare names like "TRIG" on these modules will resolve as ambiguous; always pick a specific channel. When the patch doesn't specify which channel, default to CH1. Role per use, not per identity. A module that's a modulator in one patch can be a voice in another (Maths slow-cycle vs audio-rate cycle). Pick the role for THIS patch. The enum is intentionally coarse — four buckets, not a taxonomy — so map the edge cases: - **clock** — anything emitting timing: clocks, but also trigger/gate *sequencers* and drum sequencers (a sequencer is a clock that emits a pattern). - **modulator** — CV/envelope/LFO sources shaping another module (envelopes, LFOs, random, function generators, S&H). - **voice** — anything generating the sound being processed: oscillators, drum voices, noise, sample players, physical-modeling/granular *sources*. - **processor** — anything acting *on* an incoming signal: filters, VCAs, effects (delay/reverb), waveshapers, granular/spectral *sound-processors*, and all utilities (mixers, attenuators, mults, switches). When a module both makes and processes sound, bucket by its job in THIS patch — a granular module sculpting an external input is a processor; running free as a source it's a voice. Role is currently informational — the renderer lays out by wire topology, not by role bucket — but it's still a required field, so declare it accurately for future renderer use and so the spec reads correctly. `notes[]` is patch-level prose displayed below the diagram — settings, signal-flow narration ("PNW OUT1 firing 1/16 gates", "Channel 1 cycle mode, long rise"). Errors (descriptive — they point at fixes): - "Module not found: <id>" - "Unknown jack "<name>" on <id>. Available <inputs|outputs>: ..." — pick from the list, or call get_modules - "Ambiguous jack "<name>" on <id>: matches ..." — name a specific jack from the candidates - "Patch must have at least 3 modules" - "Wire source ... is not an output" / "Wire destination ... is not an input" - "Wire to/from unknown module ref: <ref>" - "Duplicate ref: <ref>" Cross-type wires (e.g. audio into a CV input) render normally with a warning panel below the diagram — Eurorack tolerates type mismatches by design, but warnings catch unintended ones.

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• search_manual

  eurorack

  Full-text search across parsed module manuals, product pages, and firmware release notes. Use this only when the question is about content that lives in continuous prose rather than in typed fields: - Procedural: calibration sequences, button combos, factory-reset steps, save/load procedures. - Diagnostic: LED color meanings, error indicators, troubleshooting trees. - Firmware specifics beyond the short notes on firmware_versions (which only carry the headline change). - Panel walkthroughs and prose explanations that aren't captured as parameters/jacks/zones. Do NOT call this for content already in get_module: a parameter's behavior, a jack's signal type or polarity, a mode's name or description, capability tags, HP, or power draw. Those are typed and authoritative there. Do NOT call this to summarize a module — that's get_module's job. search_manual returns excerpts, not summaries. Returned chunks are source prose, not typed facts. Read them to ground your answer, then paraphrase and point the user to the source to verify (cite-and-point, not reproduce — SKILL.md §8). `text` is capped (~800 chars); the full passage is at audit_url. Args: - query (string, required): search terms. Plain words are AND'd by default ("calibration LED" matches chunks mentioning both). If the AND match returns 0 rows, the server retries with OR (any-token match) and sets _meta.relaxed_to_or=true on the response — so a long natural-language query like "cascade mode time inner outer delay" still surfaces something useful instead of zeroing out. Best practice is still 2–4 distinctive keywords; the OR fallback is a safety net, not a substitute. Query is tokenized to alphanumeric runs; FTS5 punctuation is stripped. - module_id (string, optional): "<manufacturer>/<module-slug>" — restrict to one module. Strongly recommended when the user has named a module. - source_id (integer, optional): restrict to one source. Use when you already have a source_id from get_source / list_references and want to dig into that specific document. - source_type (string, optional): one of "manual", "product_page", "firmware_notes". Defaults to all. - limit (integer, optional): default 5, max 20. Smaller is usually better — top-3 hits cover most queries. Returns: { "query": string, "matches": [{ "chunk_id": number, "source_id": number, "source_type": string, "source_title": string | null, "module_id": string | null, "heading_path": string, // "Calibration > Tuning Procedure" "snippet": string, // BM25-highlighted excerpt with [matches] in brackets "text": string, // chunk text, capped ~800 chars; paraphrase, don't paste "truncated": boolean, // true if text was trimmed; full passage at audit_url "audit_url": string, // human-readable audit page for the source "rank": number // BM25 score (more negative = better match) }], "total": number, // total matches across the corpus (capped at 200) "_meta": { "kind": "manual_excerpt", "query": <args>, "relaxed_to_or": true // only present when AND returned 0 and OR retry fired } } Examples: - "How do I calibrate Plaits' V/Oct?" → { query: "calibration V/Oct", module_id: "mutable-instruments/plaits" } - "What does the red LED on Marbles mean?" → { query: "red LED", module_id: "mutable-instruments/marbles" } - "How do I reset Pamela's New Workout to defaults?" → { query: "factory reset defaults", module_id: "alm-busy-circuits/pamelas-new-workout" } - "What changed in Plaits firmware 1.2?" → { query: "1.2", module_id: "mutable-instruments/plaits", source_type: "firmware_notes" } Errors: - Returns matches=[] with total=0 if nothing matches. Not an error. - Errors only on malformed input (missing query, invalid limit, unknown source_type).

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• get_strategy_code

  quantifyme

  Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_shot` as `community_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.

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• aws___search_documentation

  AWS Knowledge

  # AWS Documentation Search Tool Use this tool to find relevant AWS documentation — always follow up with `read_documentation` to get complete answers. Prefer this over general knowledge for AWS services, features, configurations, troubleshooting, and best practices. ## When to Use This Tool **Always search when the query involves:** - Any AWS service or feature (Lambda, S3, EC2, RDS, etc.) - AWS architecture, patterns, or best practices - AWS CLI, SDK, or API usage - AWS CDK or CloudFormation - AWS Amplify development - AWS errors or troubleshooting - AWS pricing, limits, or quotas - Strands Agents development - "How do I..." questions about AWS - Recent AWS updates or announcements **Only skip this tool when:** - Query is about non-AWS technologies - Question is purely conceptual (e.g., "What is a database?") - General programming questions unrelated to AWS ## Skill Suggestions for Actionable Queries When your search query matches tasks that benefit from domain-specific expertise, this tool will suggest relevant **Agent Skills**. Skills package domain knowledge, workflows, best practices, decision frameworks, and reference materials that make you a specialist in a particular AWS domain. **How it works:** - Your search query is scored against the skills registry using semantic search over skill descriptions and metadata tags - If your query matches a skill's domain, relevant skills are returned alongside documentation results - Skills cover a wide range of domains: deployment, troubleshooting, security, optimization, architecture, and more - To load a suggested skill, use the `retrieve_skill` tool with the `skill_name` - Once loaded, follow the skill's workflows and retrieve any referenced files as needed **Example queries that may return skills:** - "deploy a web application to AWS" — may return a deployment skill with architecture guidance and step-by-step deployment instructions - "debug Lambda cold start issues" — may return a troubleshooting skill with diagnostic workflows - "secure S3 buckets" — may return a security skill with best practices and compliance checklists - "optimize API Gateway latency" — may return a performance skill with decision frameworks - "set up VPC peering" — may return a networking skill with step-by-step procedures ## Quick Topic Selection | Query Type | Use Topic | Example | |------------|-----------|-------| | API/SDK/CLI code | `reference_documentation` | "S3 PutObject boto3", "Lambda invoke API" | | New features, releases | `current_awareness` | "Lambda new features 2024", "what's new in ECS" | | Errors, debugging | `troubleshooting` | "AccessDenied S3", "Lambda timeout error" | | Amplify apps | `amplify_docs` | "Amplify Auth React", "Amplify Storage Flutter" | | CDK concepts, APIs, CLI | `cdk_docs` | "CDK stack props Python", "cdk deploy command" | | CDK code samples, patterns | `cdk_constructs` | "serverless API CDK", "Lambda function example TypeScript" | | CloudFormation templates | `cloudformation` | "DynamoDB CloudFormation", "StackSets template" | | Architecture, blogs, guides | `general` | "Lambda best practices", "S3 architecture patterns" | | Strands Agents | `strands_docs` | "Strands Agents Python structured output", "Strands Agents AWS CDK EC2 Deployment Example" | | Domain expertise, workflows, guided procedures | `agent_skills` | "deploy serverless app", "debug Lambda cold starts", "secure IAM policies" | ## Documentation Topics ### reference_documentation **For: API methods, SDK code, CLI commands, technical specifications** Use for: - SDK method signatures: "boto3 S3 upload_file parameters" - CLI commands: "aws ec2 describe-instances syntax" - API references: "Lambda InvokeFunction API" - Service configuration: "RDS parameter groups" Don't confuse with general—use this for specific technical implementation. ### current_awareness **For: New features, announcements, "what's new", release dates** Use for: - "New Lambda features" - "When was EventBridge Scheduler released" - "Latest S3 updates" - "Is feature X available yet" Keywords: new, recent, latest, announced, released, launch, available ### troubleshooting **For: Error messages, debugging, problems, "not working"** Use for: - Error codes: "InvalidParameterValue", "AccessDenied" - Problems: "Lambda function timing out" - Debug scenarios: "S3 bucket policy not working" - "How to fix..." queries Keywords: error, failed, issue, problem, not working, how to fix, how to resolve ### amplify_docs **For: Frontend/mobile apps with Amplify framework** Always include framework: React, Next.js, Angular, Vue, JavaScript, React Native, Flutter, Android, Swift Examples: - "Amplify authentication React" - "Amplify GraphQL API Next.js" - "Amplify Storage Flutter setup" ### cdk_docs **For: CDK concepts, API references, CLI commands, getting started** Use for CDK questions like: - "How to get started with CDK" - "CDK stack construct TypeScript" - "cdk deploy command options" - "CDK best practices Python" - "What are CDK constructs" Include language: Python, TypeScript, Java, C#, Go **Common mistake**: Using general knowledge instead of searching for CDK concepts and guides. Always search for CDK questions! ### cdk_constructs **For: CDK code examples, patterns, L3 constructs, sample implementations** Use for: - Working code: "Lambda function CDK Python example" - Patterns: "API Gateway Lambda CDK pattern" - Sample apps: "Serverless application CDK TypeScript" - L3 constructs: "ECS service construct" Include language: Python, TypeScript, Java, C#, Go ### cloudformation **For: CloudFormation templates, concepts, SAM patterns** Use for: - "CloudFormation StackSets" - "DynamoDB table template" - "SAM API Gateway Lambda" - "CloudFormation template examples" ### strands_docs **For: Strands Agents API reference, integrations, model providers, session managers, tools, examples, user-guide** Use for: - "Strands Agents Python SDK example" - "Strands Agents AWS integration" - "Strands Agents community contributions" - "Strands Agents usage examples" - "Strands Agents usage guide" ### general **For: Architecture, best practices, tutorials, blog posts, design patterns** Use for: - Architecture patterns: "Serverless architecture AWS" - Best practices: "S3 security best practices" - Design guidance: "Multi-region architecture" - Getting started: "Building data lakes on AWS" - Tutorials and blog posts **Common mistake**: Not using this for AWS conceptual and architectural questions. Always search for AWS best practices and patterns! **Don't use general knowledge for AWS topics—search instead!** ### agent_skills **For: Discovering agent skills — domain-specific expertise packages for AWS workflows** Use for: - Complex tasks that benefit from guided workflows: "deploy a serverless application" - Troubleshooting scenarios: "debug Lambda cold starts", "resolve ECS task failures" - Security and compliance: "secure S3 buckets", "review IAM policies for least privilege" - Architecture and optimization: "optimize API Gateway latency", "design multi-region architecture" - When you need domain expertise beyond what documentation provides Skills go beyond documentation — they provide workflows, decision frameworks, best practices, and may include embedded procedures for critical sub-tasks. **Important**: This topic is meant for discovery. Once you identify the skill you need, use `retrieve_skill` tool with the `skill_name` to load the full skill and its reference materials. **Note**: If combined with other topics, skills will be mixed into the documentation results. Use `agent_skills` alone for a clean skill-only listing. ## Search Best Practices **Be specific with service names:** Good examples: ``` "S3 bucket versioning configuration" "Lambda environment variables Python SDK" "DynamoDB GSI query patterns" ``` Bad examples: ``` "versioning" (too vague) "environment variables" (missing context) ``` **Include framework/language:** ``` "Amplify authentication React" "CDK Lambda function TypeScript" "boto3 S3 client Python" ``` **Use exact error messages:** ``` "AccessDenied error S3 GetObject" "InvalidParameterValue Lambda environment" ``` **Add temporal context for new features:** ``` "Lambda new features 2024" "recent S3 announcements" ``` **If the first search does not return results that directly answer the question, refine your query and search again with different terms, a more specific phrase, or a different topic. Try conceptual/architectural topics (general, blogs) if reference docs are too narrow.** **After searching, use `read_documentation` on the top-ranked URLs to verify and complete your answer.** ## Multiple Topic Selection You can search multiple topics simultaneously for comprehensive results: ``` # For a query about Lambda errors and new features: topics=["troubleshooting", "current_awareness"] # For CDK examples and API reference: topics=["cdk_constructs", "cdk_docs"] # For Amplify and general AWS architecture: topics=["amplify_docs", "general"] # For actionable tasks: topics=["agent_skills"] ``` ## Response Format Results include: - `rank_order`: Relevance score (lower = more relevant) - `url`: Direct documentation link — use with `read_documentation` to get the full page content - `title`: Page title - `context`: Partial excerpt only — not the complete documentation. After reviewing results, call `read_documentation` on the most relevant URLs before answering. Do not answer based on the context excerpt alone. ## Parameters ``` search_phrase: str # Required - your search query topics: List[str] # Optional - up to 3 topics. Defaults to ["general"] limit: int = 5 # Optional - max results per topic ``` --- **Remember: When in doubt about AWS, always search. This tool provides the most current, accurate AWS information. But search is only step 1 — always read the full documentation to give complete answers.**

  Connector

• search_modules

  eurorack

  Search the corpus for Eurorack modules matching a combination of filters. Filters compose with AND. Omit any filter to leave that dimension unrestricted. The result is sorted by module name; pagination metadata in the response envelope lets you page through long result sets. Args: - capability (string): capability id, e.g. 'envelope-generator', 'clock-source'. Run a search with NO capability filter to get the full capability taxonomy (ids + labels + counts) in _meta.taxonomy. Retired/variant slugs resolve via the capability_aliases layer (e.g. 'low-pass-gate' → 'lowpass-gate', 'quantiser' → 'quantizer'), so either form is accepted. - manufacturer (string): manufacturer id, e.g. 'make-noise', 'mutable-instruments'. - hp_min, hp_max (number): module width in HP. hp_max=10 finds modules ≤ 10 HP. - signal_type_in (string): the module accepts a jack of this signal type as input. One of audio, cv, gate, trigger, clock, mixed. signal_type_in='audio' and ='cv' both also match jacks tagged 'mixed' (the schema's value for jacks the source describes as accepting both audio and CV — e.g. Joranalogue Compare 2's signal inputs); the other values match literally. - signal_type_out (string): the module produces a jack of this signal type as output. Same 'mixed'-superset semantics as signal_type_in. - text (string): free-text match against module id, name, slug, description, and the ids/labels/descriptions of capabilities the module has (case-insensitive substring). Matches hyphenated forms like "filter-8" against the slug/id even when the display name uses a space ("Filter 8"), and is whitespace-insensitive on id/slug/name so "3x MIA" finds the module named "3xMIA". Capability-label coverage means text="multiband" finds modules tagged multiband-filter without knowing the kebab-case id, and a curated alias layer extends that to common word-form variants ("multi-output" / "multi-band" / "band-split" → multiband-filter, "low-pass" → lowpass-filter, retired ids like "voltage-controlled-filter" → vcf). Truly novel wording still requires the _meta.taxonomy overview (run a no-capability search); if you expected a hit and got 0, call report_gap so the alias can be added. - voct_tracking_range_min (number): the module has a V/Oct input whose source-stated tracking range is at least this many octaves. Use for "filters that track 5+ octaves" / "oscillators with wide V/Oct range". - voct_tracking_quality (string): the module has a V/Oct input with this tracking quality, one of 'calibrated', 'temperature-compensated', 'approximate', 'uncalibrated'. 'temperature-compensated' is the strongest claim. - voct_temperature_compensated (boolean): the module has a V/Oct input whose source explicitly states temperature compensation. Implies calibrated but separately flagged because some manuals call out only one. - audio_outputs_min (number): the module has at least this many output jacks with signal_type='audio'. Use for "multi-output filters" (≥3 audio outs surfaces LP/BP/HP-tap VCFs like Three Sisters, QPAS, A-108, Polaris) or any multi-tap audio module. Combine with capability='vcf' for the canonical multi-output-filter query. - limit (number): default 50, max 200. - offset (number): pagination offset. Returns: { "modules": [{ id, name, manufacturer, hp, capabilities: [string], description, production_status }], "total": number, // total matches (across all pages) "_meta": { "query": <args>, // Present whenever a 'capability' filter matched >=1 module (NOT gated on // total=0 — it accompanies normal results). The category-coverage // denominator, so a "best X" recommendation can self-caveat instead of // reading as "best available": // On a no-capability search: the global capability taxonomy (id, label, // description, module_count) — discover the controlled vocabulary here // instead of a separate list_capabilities call. "taxonomy": [{ "id": "lowpass-gate", "label": "Low-pass gate", "module_count": 19 }], "coverage": { "capability": "stereo-mixer", // the capability you filtered on "category_total": 9, // modules in the corpus with this capability, IGNORING your other filters "corpus_total": 388, // all modules in the corpus "note": "...best of 9 in the corpus, not best available..." // ready-to-use recommendation caveat }, // Present when the server's token-AND fallback rescued an otherwise-empty // phrase query (e.g. "pamela workout" → "Pamela's NEW Workout" via per-word // identifier match). Not an error; just signals that results came from the // relaxed pass rather than the literal phrase. "relaxed_to_tokens": true, // On total=0 (after the token-AND fallback has already been attempted), the // server adds these diagnostic hints so you can retry productively in one // turn instead of guessing variants. Each is independently optional: "would_match_without": ["capability", "text"], // filters that, if individually dropped, would yield ≥1 result — the named filter(s) cost you the match "closest_text_hits": [{ id, name, manufacturer }], // top 3 modules matching 'text' alone (other filters dropped); inspect for a close hit you filtered out by accident "did_you_mean": [{ id, name, manufacturer }], // top 3 edit-distance neighbors of 'text' when it matched nothing literally (a single-token typo like "multgrain" → multigrain); PRESENT means retry with the suggested id, ABSENT means the term is a genuine corpus gap (call report_gap) — the discriminator would_match_without can't give you "capability_suggestions": [{ id, label }], // top 3 valid capabilities matching the 'capability' arg you passed (only set when the arg wasn't a known slug or alias) — use list_capabilities for the full taxonomy "manufacturer_suggestions": [{ id, name }], // top 3 maker slugs matching the 'manufacturer' arg (only set when it wasn't a canonical slug) — the manufacturer arg is EXACT-match, so e.g. "addac" → "addac-system", "nonlinearcircuits" → "nlc"; retry with the suggested id "feedback_hint": "..." // fallback prompt to call report_gap when no other diagnostic applies } } Examples: - "What envelope generators under 8 HP exist?" → {capability: 'envelope-generator', hp_max: 8} - "What ALM modules are in the corpus?" → {manufacturer: 'alm-busy-circuits'} - "What clock sources are there?" → {signal_type_out: 'clock'} - "Modules with 'workout' in the name" → {text: 'workout'} - "Filters that track V/Oct over 5 octaves" → {capability: 'vcf', voct_tracking_range_min: 5} - "Temperature-compensated filter cores" → {voct_tracking_quality: 'temperature-compensated'} - "Multi-output filters with LP/BP/HP taps" → {capability: 'vcf', audio_outputs_min: 3} Errors: - Returns an empty modules array (and total=0) if nothing matches. Not an error — inspect _meta.would_match_without / closest_text_hits / capability_suggestions / manufacturer_suggestions to decide whether to broaden the query or call report_gap. - Invalid filter values pass through to the WHERE clause; if no module satisfies them you get total=0. After picking a hit, call get_module with the id for full details.

  Connector

• search_techniques

  eurorack

  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.

  Connector

• rent-browser

  mcp-server

  [Auth Required + Active] Get credentials to rent a real Chrome browser. Install CLI: `pip install ceki-sdk` (Python) or `npm install -g @ceki/sdk` (Node). Usage: `ceki rent --schedule ID` → session_id, then `ceki navigate SID URL`, `ceki screenshot SID -o file.png`, `ceki stop SID`. Per-minute billing from AgentWallet. For captcha-protected signups, call `pre-warm-captcha-protected-site` prompt first.

  Connector

• search_datanexus_tools

  DataNexus MCP

  Find the right DataNexus tool by describing your task in plain English. Read-only. No side effects. Call this before any other DataNexus tool to reduce context load from 40000 to 800 tokens. query: Plain English description of your task e.g. check if a Python package has CVEs or look up a UK charity by name. Required. domain: Restrict results to one sub-server: nonprofit, security, compliance, domain, legal, govcon, or regulatory. Optional. Returns matching tool names and parameter hints you can call directly. Do not call this recursively or to validate results — use validate_tool_output for that. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="search_datanexus_tools", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

  Connector

• analyze_record

  Senzing

  Get the Senzing JSON analyzer script to validate mapped data files client-side. REQUIRED: `workspace_dir` (writable directory, e.g. ~/sz-workspace) — the call WILL FAIL without it. The analyzer validates records against the Entity Specification, examines feature distribution, attribute coverage, and data quality. Returns a Python script (no dependencies) with instructions. No source data is sent to the server. Typical workspace_dir values: Linux `/tmp` or `~/sz-workspace`; macOS `~/sz-workspace`; sandboxed envs: explicit path under home (do NOT assume /tmp exists).

  Connector

• get_module

  eurorack

  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.

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• acc_create_rfi

  ScanBIM MCP

  Create a Request For Information in ACC Build's RFIs module via the APS Construction RFIs v1 API, in 'draft' status. Returns the ACC rfi_id. When to use: a trade or subcontractor needs formal information from the design team (unclear detail, conflicting spec, missing dimension) and you want a tracked paper trail. When NOT to use: the item is just a punchlist fix — use acc_create_issue. The question is internal to one trade — handle inside that trade's toolchain. APS scopes: data:read data:write account:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied (app not provisioned for the project's ACC account, or RFIs module not enabled); 404 project_id not found — check the ID; 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: NON-IDEMPOTENT. Creates a new draft RFI each call. Inserts a row into D1 usage_log.

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