"Automation" matching MCP tools:

• automation_test

  Perspective AI

  Runs a single end-to-end execution of an existing automation against a mock conversation, returning success/failure plus the channel target and duration. Mirrors a real production firing. Behavior: - Sends REAL messages by default: posts the configured webhook, sends the configured email, posts the Slack message, or writes the HubSpot record. Use override_email (email channels) or override_webhook (webhook channels) to redirect delivery to a safe test target. - Each call fires another real delivery. - Errors when the perspective or automation is not found, or you do not have access. Webhook URLs (configured or override) are validated. - Mock conversation defaults: trust score 85, status complete, "Test Participant" / test@example.com. Override participant_name, summary, and tags via test_data. - Returns success: true also when the automation's condition skips delivery (e.g., tag/trust filter doesn't match the mock). The error field is populated only on real delivery failures. When to use this tool: - Verifying a freshly-created automation actually delivers before relying on it (override_email / override_webhook direct the test to a safe target instead of real recipients). - Reproducing a delivery failure surfaced in automation_list (last_error). When NOT to use this tool: - Listing what's configured — use automation_list. - Changing config — use automation_update. - Removing the automation — use automation_delete.

  Connector

• tm_import_rvt

  Twinmotion MCP

  Import a Revit/BIM model into the Twinmotion visualization pipeline: downloads the source file from a public URL, uploads it to an APS OSS transient bucket, and kicks off an SVF2 + thumbnail translation job. Returns the base64 URN (project_id) used by every other tm_* tool. When to use: when a user wants to prepare a Revit (.rvt), IFC (.ifc), or other BIM/CAD model for real-time visualization in Unreal Engine / Twinmotion — typically the first step before rendering stills, defining scenes, or exporting FBX/glTF/OBJ geometry for a UE import. Also use when you need thumbnails or view metadata from a source file that has not yet been translated by APS. When NOT to use: not for MEP clash review (use navisworks-mcp), not for quantity takeoff or cost estimation (use qto-mcp), not for Twinmotion presets editing — Twinmotion itself has no public REST API, so scene/material authoring must happen manually in the UE editor after FBX/USD export. APS scopes required: data:read data:write data:create bucket:read bucket:create viewables:read. Uses Model Derivative API (translation) + OSS (upload). Twinmotion has no public REST API; all automation is APS Model Derivative + manual Unreal Engine export. Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; large .rvt/.nwd/.ifc files are often multi-GB and translation can take 5–60 min — poll the manifest with exponential backoff (start 5s, cap 60s) rather than retrying this tool. Worker request ceiling is ~100MB body; extremely large files may need signed-URL upload instead. Errors: 401 = APS token failed (check APS_CLIENT_ID/APS_CLIENT_SECRET, re-auth); 403 = scope missing (bucket:create/data:write not granted — have user re-consent); 404 = file_url unreachable; 409 = bucket key collision (rare — retry, tool uses timestamp); 413/507 = file too large for worker memory (advise signed-URL upload); 422 = unsupported source format (only Autodesk-accepted types: rvt, ifc, nwd, dwg, dgn, 3dm, stp, etc.); 429 = back off 60s before retrying; 5xx = APS upstream outage, retry with backoff. Side effects: CREATES a new transient OSS bucket (scanbim-viz-<timestamp>, auto-expires in 24h), CREATES an object in OSS, STARTS a translation job consuming APS cloud credits. NOT idempotent — each call creates a new bucket + URN. Writes a row to usage_log D1 table.

  Connector

• acc_upload_file

  ACC MCP

  Upload a file from a public source URL into an ACC project folder. Runs the full four-step APS Data Management flow: top-folder discovery → storage object creation → OSS PUT of bytes → first-version item creation. When to use: The user wants to push a document/photo/model into ACC Docs — e.g. 'upload this site photo to the Tower project Photos folder' or an automation needs to archive an exported report into Project Files. When NOT to use: Do not use for files already in ACC; do not use for files behind auth-gated URLs (fetch step is an unauthenticated GET). For very large files (>100MB), prefer the chunked/signed-S3 upload flow, not this single-PUT implementation. APS scopes: data:read data:write data:create account:read. Rate limits: APS Data Management ~50 req/min per endpoint; OSS upload bandwidth typically 100 MB/min per app. This tool issues 3–5 APS calls per upload, so budget accordingly. Errors: 401 (APS token expired — refresh); 403 (user lacks folder write permission — ask account admin to grant 'Edit' on folder); 404 (project_id not found or folder_path does not match any top folder — verify 'b.' prefix, hub membership, and folder name); 422 (invalid file_name or conflicting version); 429 (rate limit — back off 60s); 5xx (ACC/OSS upstream — retry with jitter BUT be cautious: storage object may already be created so reuse, do not re-create). Also: if source file_url returns non-2xx, the tool throws before touching ACC. Side effects: Creates a storage object, uploads bytes, and creates a versioned item in the target folder. NOT idempotent — a retry may create a duplicate item with a new version. Surface the returned item_id to the user to avoid re-uploads.

  Connector

• automation_update

  Perspective AI

  Updates fields on an existing automation. Pass a partial updates object with only the fields you want to change; omitted fields are preserved. Toggling enabled or changing schedule/channel/condition takes effect on the next scheduled run. Behavior: - Saves the change to the same automation record. Scheduled automations with an active workflow are restarted on update so the next run picks up the latest config. - Errors when the perspective or automation is not found, or you do not have access. - Webhook URLs in updates are validated. For HubSpot, the workspace's HubSpot connection is re-checked — errors with "Could not resolve HubSpot portal ID — please reconnect HubSpot" if disconnected. - For scheduled automations: changes to channel, condition, execution mode, instruction, or message template apply starting from the next run, not the one currently in flight. When to use this tool: - Toggling enabled on or off (also pauses/resumes scheduled sends). - Changing schedule, channel, condition, instruction, or message_template on a live automation. When NOT to use this tool: - Removing the automation entirely — use automation_delete. - Verifying a config change actually delivers — follow up with automation_test. - Listing what's configured — use automation_list.

  Connector

• score_prompt

  pqs-mcp-server

  Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.

  Connector

• manage_substrate

  mcp

  Manage the entire Butterbase substrate: the action ledger, the entity graph, source artifacts, institutional memory (decisions/commitments/learnings), outbox, attention rules, snapshots, and settings. Every substrate write goes through the action ledger via "propose". The ledger captures the proposer, the policy verdict, and the result; nothing is written to the substrate without an action_id you can trace. Actions — Writes (action ledger): - "propose": Propose a write. capability ∈ {upsert_entity, update_entity, patch_entity, record_decision, record_commitment, record_learning, upsert_source_artifact, merge_entities, delete_entity, bulk_revert_actions, record_principle, retire_principle, supersede_decision, send_email_draft, revert_action}. Body: { capability, payload, dangerously_skip_approval?, idempotency_key? }. When idempotency_key matches a prior action (per substrate user, no TTL), the response returns that prior action's verdict + result and includes replay: true. - "approve": Approve a previously-proposed action that is awaiting review. { action_id }. - "reject": Reject a previously-proposed action. { action_id, reason }. Actions — Ledger reads: - "list_actions": List action ledger rows. Optional: { status?, capability?, source_app_id?, source_rule_id?, limit?, before? }. - "get_action": Get one ledger row by id. { action_id }. Actions — Entity graph: - "find_entities": List/fuzzy-search entities. Optional: { type?, q?, primary_email?, limit? (max 1000), cursor? }. type ∈ person|company|fund|workspace|team|project|event|agent|self. Returns { entities, next_cursor? }. - "get_entity": Get one entity by id. { entity_id }. - (writes use "propose" with capability=upsert_entity / update_entity / patch_entity / merge_entities / delete_entity) Actions — Source artifacts (meeting transcripts, email threads, call recordings, etc.): - "list_source_artifacts": List/search. Optional: { kind?, q?, limit?, count? }. q runs FTS over title+summary+content. - "get_source_artifact": Get one artifact by id (incl. full content). { artifact_id }. - (writes use "propose" with capability=upsert_source_artifact) Actions — Institutional memory: - "search_memory": FTS across decisions, commitments, learnings, and source_artifacts. { q?, kinds?, limit? }. q is optional — omit to list recent items. kinds = comma-separated subset of {decisions,commitments,learnings,source_artifacts}. - "list_memory": Browse memory chronologically (no FTS). Filter by source_artifact_id, kinds, superseded. { kinds?, source_artifact_id?, superseded?, before?, limit? }. - (writes use "propose" with capability=record_decision / record_commitment / record_learning / record_principle / supersede_decision / retire_principle) Actions — Outbox (side-effect dispatch queue): - "list_outbox": List rows. Optional: { status?, limit? }. status ∈ pending|in_flight|succeeded|failed|dead_letter|cancelled. - "retry_outbox": Manually re-queue a failed/dead-letter row. { outbox_id }. - "cancel_outbox": Mark a not-yet-completed row as cancelled. { outbox_id }. Actions — Attention rules (cron-triggered automation): - "list_rules": List all attention rules. Optional: { enabled? }. - "get_rule": Get one rule by id. { rule_id }. - "create_rule": Create a new rule. { rule: {name, description?, trigger_cron, condition_mode, condition, action_capability, action_payload_template, enabled?, max_fires_per_day?} }. - "update_rule": Full replace by id. { rule_id, rule }. - "delete_rule": Delete by id. { rule_id }. - "enable_rule": Set enabled=true. { rule_id }. - "disable_rule": Set enabled=false. { rule_id }. - "list_rule_firings": History for one rule. { rule_id, status?, limit?, before? }. Actions — Snapshots & settings: - "snapshots": Daily aggregate snapshots. Optional: { days? }. - "get_settings": Show substrate settings (e.g. yolo_mode). - "set_yolo": Toggle YOLO mode. { yolo_mode: boolean }. Capability default policies (what happens when you "propose" each capability): record_decision → auto (reversible) record_commitment → auto (reversible) record_learning → auto (reversible) upsert_entity → auto (reversible) update_entity → auto (reversible) patch_entity → auto (reversible) upsert_source_artifact → auto (reversible) revert_action → auto (not reversible) record_principle → approval_required (not reversible) ← mutates policy layer supersede_decision → approval_required (not reversible) ← mutates policy layer retire_principle → approval_required (not reversible) ← mutates policy layer delete_entity → approval_required (not reversible) merge_entities → approval_required (not reversible) bulk_revert_actions → approval_required (not reversible) send_email_draft → approval_required (not reversible; yolo_eligible but agent proposals always gate) Note: record_decision auto-approves, but record_principle / supersede_decision / retire_principle always require human approval — they mutate the policy-enforcement layer of the substrate. Common returns: the raw substrate API JSON response. The agent has full CRUD over the substrate via this single tool — there is no other substrate tool to call.

  Connector

• read_account_info

  Arcadia Finance

  Get full overview of an Arcadia account: health factor, collateral value, debt, deposited assets, liquidation price, and automation status. Health factor = 1 - (used_margin / liquidation_value): 1 = no debt (safest), >0 = healthy, 0 = liquidation threshold, <0 = past liquidation. Higher is safer. On all supported chains returns an `automation` object showing which asset managers are enabled (rebalancer, compounder, yield_claimer, merkl_operator, gas_relayer, cow_swapper). Automation detection spans every asset-manager version deployed on the selected chain, so registrations made on older versions are still reported as active; the returned value is the user-facing dex_protocol (e.g. 'slipstream') with no version suffix. LP positions in assets[] include a dex_protocol field (slipstream, slipstream_v2, slipstream_v3, staked_slipstream, staked_slipstream_v2, staked_slipstream_v3, uniV3, uniV4) — use this as the dex_protocol param for write_asset_manager.* tools. Slipstream V2 is Base-only. V3 is available on Base and Optimism. Unichain supports only Slipstream V1, uniV3, and uniV4. The automation object uses internal AM key names (slipstreamV1, slipstreamV2, slipstreamV3, uniV3, uniV4): map slipstreamV1 → 'slipstream'/'staked_slipstream', slipstreamV2 → 'slipstream_v2'/'staked_slipstream_v2', slipstreamV3 → 'slipstream_v3'/'staked_slipstream_v3', uniV3 → 'uniV3', uniV4 → 'uniV4'. Numeric fields without a _usd suffix are in the account's numeraire token raw units (divide by 10^decimals: 6 for USDC, 18 for WETH, 8 for cbBTC). Fields ending in _usd are in USD with 18 decimals (divide by 1e18). health_factor is unitless. Asset amounts are raw token units. To list all accounts for a wallet, use read_wallet_accounts.

  Connector

• score_prompt

  PQS - Prompt Quality Score

  Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.

  Connector

• automation_create

  Perspective AI

  Creates an automation on a perspective. Triggers: per_interview (fires on every completed conversation) or scheduled (daily/weekly digest). Channels: webhook, email, slack, hubspot. Execution modes: direct (fast, deterministic) or agent (LLM-powered). Behavior: - Each call creates a new automation — even if name/config matches an existing one. - Once enabled, the automation starts firing on real events: per_interview sends on every completed conversation going forward; scheduled sends a real message on the configured cadence (daily/weekly). - Webhook URLs are validated. For HubSpot, the workspace's HubSpot connection is required — errors with "Could not resolve HubSpot portal ID — please reconnect HubSpot" if not connected. - Errors when the perspective is not found or you do not have access. When to use this tool: - The user wants ongoing notifications on every completed conversation (per_interview). - Building a daily/weekly digest delivered to Slack, email, HubSpot, or a webhook (scheduled). When NOT to use this tool: - Trying a one-off send before going live — create the automation, then use automation_test (use override_email / override_webhook to avoid hitting real recipients). - Editing or toggling an existing automation — use automation_update. - Connecting Slack or HubSpot — use integration_manage first; the provider must be connected before slack/hubspot channels work. Example — per-conversation Slack notify: ``` { "perspective_id": "...", "automation": { "name": "Notify Slack", "trigger": { "type": "per_interview" }, "execution_mode": "agent", "channel": { "type": "composio", "delivery_config": { "provider": "slackbot", "tool_slug": "SLACKBOT_SEND_MESSAGE", "params": { "channel": "#research" }, "resource_id": "...", "resource_name": "..." } } } } ``` Typical flow: 1. integration_manage (operation: "list"/"connect") → ensure Slack / HubSpot is connected (only needed for those channels) 2. automation_create → create the automation 3. automation_test (with overrides) → verify delivery before relying on it

  Connector

• taskbounty_security_check

  TaskBounty

  Scan a PUBLIC GitHub repo for GitHub Actions + CI security/maintenance hygiene before launch — ideal for apps built with Lovable, Bolt, Replit, Cursor, or v0 ("is my AI-built app safe to ship?"). Returns a safe summary: findings by category with counts, an unlisted report URL, and fix options. SCOPE, honestly: it checks GitHub Actions workflow + update-automation hygiene only — it does NOT check exposed secrets, auth, payments, webhooks, or runtime behavior, which need a manual review. No API key required. For PRIVATE repos, tell the user to run `npx taskbounty-check .` locally so their source never leaves their machine.

  Connector

• write_asset_manager_cow_swapper

  Arcadia Finance

  Encode args for standalone direct CowSwap mode. Enables the CowSwapper to swap any ERC20 → ERC20 via CoW Protocol batch auctions (MEV-protected). Unlike compounder_staked or yield_claimer_cowswap, this is NOT coupled to any other automation — each swap requires an additional signature from the account owner. Only available on Base (8453). Returns { asset_managers, statuses, datas } — pass to write_account_set_asset_managers. Combinable with other intent tools.

  Connector

• tronsave_update_auto_sell_setting

  TronSave MCP Server

  Update the existing auto-sell configuration with partial fields. Returns the updated `autoSettings` payload. Side effect: overwrites stored automation settings for the current user; not idempotent across different field sets. Requires a signature session and `mcp-session-id`. Use for INCREMENTAL changes after registration; read the baseline via `tronsave_get_user_auto_setting` to avoid accidental resets, and use `tronsave_register_auto_sell` only for first-time setup. Fails for invalid field combinations, unauthorized sessions, or policy constraints.

  Connector

• automation_delete

  Perspective AI

  Permanently deletes an automation. Pauses any scheduled sends first, then removes the automation. Behavior: - DESTRUCTIVE and irreversible — the automation cannot be recovered. No undo. - Errors when the perspective or automation is not found, or you do not have access. Deleting an already-deleted automation errors as well. - If pausing the scheduled sender fails, the deletion is aborted and you'll get success: false with "Failed to stop running workflow. Please try again." — the automation stays intact in that case. When to use this tool: - The user explicitly asked to remove an automation and confirmed. - Cleaning up a misconfigured automation that automation_test repeatedly fails on. When NOT to use this tool: - The user just wants to pause it temporarily — use automation_update with { enabled: false } instead. - You're not sure which automation_id is correct — confirm via automation_list first.

  Connector

• search_mcp_servers

  Swarm Tips — Aggregated AI Agent Activities

  [IN DEVELOPMENT] [READ] Search the Layer 3 curated directory of MCP servers and agent-work tools. The directory has 30 entries across three vetting tiers — `first-party` (operated by the swarm.tips DAO), `vetted` (third-party, we've used + verified), `discovered` (cataloged from public sources, not yet exercised). Filter by `query` (substring vs name/description/tags), `category` (substring), and `tier`. Results sort first-party → vetted → discovered. The same directory powers swarm.tips/discover; this tool exposes it programmatically. Use this when an agent needs to find an MCP server for a capability (DeFi, search, browser automation, etc.) instead of an opportunity (which `discover_opportunities` covers).

  Connector

• hires_list_application_stage_history

  100Hires - AI ATS & Recruitment Software

  Get the full chronological stage transition history for an application, including the initial assignment. Each entry has from_stage_id/name, to_stage_id/name, moved_at (Unix seconds), moved_by_type (system, user, automation), moved_by_user_id, and source (what caused the transition, e.g. 'apply:indeed', 'form_watcher', 'user'; null for historical records). Use this for funnel analysis, attribution reports, and time-in-stage reports instead of paginating through /candidates/{id}/activities when only stage data is needed.

  Connector

• get_product_recommendation

  Custom Blinds Shop

  Translate a customer's primary concern into a product recommendation. primary_concern must be one of: blockout, heat, glare, moisture, privacy, security, automation. Optionally narrow by room (bedroom, lounge, etc.), location, budget, and aesthetic. Returns a recommended product_id with rationale — pass it to get_price or configure_product next. Security concern routes to brochure MCP (Garden Route customers only).

  Connector

• get_category_landscape

  StackSwap

  Full map of one GTM category — leaders, runner-ups, and skip/replace candidates. Returns every catalogued tool in the bucket with cost, AI-readiness, swap-registry status, and partner sign-up links. Use when the user wants to see the full landscape for a category (e.g. 'show me all CRMs', 'what outbound tools exist', 'map the analytics category') — strictly more comprehensive than `recommend_partner` (single best pick). Known buckets: crm, outbound, data, marketing-automation, analytics, meetings, support, scheduling, automation, seo, cdp, revenue-intelligence, chat, collaboration, phone, landing-pages, linkedin, ai-content, saas-mgmt, enablement, ai-tooling.

  Connector

• get_sample_data

  Senzing

  Get real sample data from CORD (Collections Of Relatable Data) datasets. Use dataset='list' to discover available datasets, source='list' to see vendors within a dataset. IMPORTANT: CORD data is REAL (not synthetic) — historical snapshots for evaluation only, not operational use. Always inform the user of this. When records are returned, a 'download_url' in the citation provides a way to fetch the full dataset. In HTTP mode this is a URL the user (or an automation) can curl; in stdio mode it is a `sz-mcp-coworker extract` command the user runs locally to pull bytes from the embedded bundle. Always present the fetch instruction to the user. Do NOT download it yourself or dump raw records into the conversation — the inline records are a small preview of the data shape. Asset IDs are not stable across versions. If a previously-known ID fails to extract, call this tool again to obtain the current ID.

  Connector

• integration_manage

  Perspective AI

  Read-only inspector for workspace integrations. Operations: "list" enumerates the registered providers (currently slackbot, hubspot, gmail) and connection status; "connect" returns a setup URL the user opens in a browser to complete OAuth; "search_tools" returns the available action slugs (e.g., SLACKBOT_SEND_MESSAGE, HUBSPOT_SUBMIT_FORM, GMAIL_SEND_EMAIL) for a connected provider. Behavior: - Read-only. Does NOT itself perform OAuth — "connect" just hands a setup URL back so the user can finish the connection in the web app. - Errors when the workspace is not found or you do not have access. - search_tools returns success: false with "No active <provider> connection. Use 'connect' operation first." when the provider is not connected. Limit is 10 tools per search. - Required params per operation: connect needs provider; search_tools needs provider and query. Otherwise returns success: false with the missing-param error. When to use this tool: - Checking which integrations the workspace has connected before configuring an automation that talks to one of them. - Surfacing the setup URL to the user when they want to connect a provider. - Discovering action slugs to populate provider-backed automations. When NOT to use this tool: - Creating or modifying automations — use automation_create / automation_update after the provider is connected. - Sending a real message to test a provider wiring — create the automation first, then run automation_test. Examples: - List: `{ "operation": "list" }` - Connect: `{ "operation": "connect", "provider": "slackbot" }` - Search: `{ "operation": "search_tools", "provider": "hubspot", "query": "create contact" }`

  Connector

• request_pilot_key

  PoolParty Main Stage Airtime

  Request pilot access to PoolParty MCP protected tools. No auth required. Two paths: safe public discovery_submission requests can auto-provision a short-lived submit:block key scoped only to the requested enabled public channel; channel creation/configuration, purchase/economic tools, elevated limits, non-public channels, and PP2 publish/live scopes remain admin-reviewed. Use this when you need protected MCP automation beyond public read/discovery tools.

  Connector