"Help Scout" matching MCP tools:

• entity_profile

  Inspire Hep

  Get everything about a company in one call. Use when a user asks "tell me about X", "give me a profile of Acme", "what do you know about Apple", "research Microsoft", "brief me on Tesla", or you'd otherwise need to call 10+ pack tools across SEC EDGAR, SEC XBRL, USPTO, news, and GLEIF. Returns recent SEC filings, latest revenue/net income/cash position fundamentals, USPTO patents matched by assignee, recent news mentions, and the LEI (legal entity identifier) — all with pipeworx:// citation URIs. Pass a ticker like "AAPL" or zero-padded CIK like "0000320193".

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• chiefmo_launch_product

  ChiefLab

  Repo-aware launch operator for agent-built products. USE WHEN the user has just finished building, shipping, or deploying a product and the message matches any of these trigger phrases: 'launch this' · 'launch what I just built' · 'help me launch' · 'get users' · 'get our first users' · 'find users' · 'market this' · 'do marketing for this' · 'announce this' · 'write the launch announcement' · 'post this' · 'post the launch' · 'publish this' · 'Product Hunt' · 'ship to Product Hunt' · 'go to market' · 'what to do after launch'. This is the PRIMARY ChiefLab entry point — call this first, not chiefmo_diagnose_marketing (which is only for diagnosing an EXISTING marketing program). If you are a coding agent (Cursor, Claude Code, Codex), gather repoContext (whatChanged, recentCommits, changedFiles, routes, readme, targetCustomer, launchGoal) BEFORE calling — repo grounding is what makes outputs reference the actual product instead of reading like 'launch any SaaS.' Returns: launchPack (per-channel drafts for LinkedIn / X / Hacker News / Reddit / Product Hunt / email / landing hero) + publishActions (approval-gated, with actionIds) + agentGuide.renderInChat (per-channel content to render inline in IDE chat) + agentGuide.nextToolCalls.primary.perChannel (chiefmo_approve_action calls keyed by channel) + reviewUrl (FALLBACK only — for phone/multi-person approval). IDE-NATIVE FLOW: render each channel's draft inline in chat, wait for user to say 'approve <channel>' or 'approve all', call chiefmo_approve_action per approved action. The reviewUrl is a side channel — surface it as 'approve from your phone here' not as the primary instruction.

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• pipeworx_feedback

  Inspire Hep

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• resolve_entity

  Inspire Hep

  Look up the canonical/official identifier for a company or drug. Use when a user mentions a name and you need the CIK (for SEC), ticker (for stock data), RxCUI (for FDA), or LEI — the ID systems that other tools require as input. Examples: "Apple" → AAPL / CIK 0000320193, "Ozempic" → RxCUI 1991306 + ingredient + brand. Returns IDs plus pipeworx:// citation URIs. Use this BEFORE calling other tools that need official identifiers. Replaces 2–3 lookup calls.

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• remember

  Inspire Hep

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• send_message

  Dock

  Send a direct message to another agent or human in the messaging substrate. Wires through cue.dock.svc, the same path the /live UI uses, so the recipient sees this message in their drawer (and, once they have a Dock-connected agent worker running, their agent harness's inbox). Address format is `<agent_slug>@<user_slug>`: `flint@socrates` targets the `flint` agent owned by user `socrates`; `self@<user_slug>` targets a human's synthetic self-agent (use this to message a human directly when you don't know which of their agents to ping). Use this when an agent legitimately needs to ask a teammate (human or agent) for help, hand off work, or follow up async; don't use it as a chat-ops side-channel for things that belong in workspace events. Sender identity follows the caller: agent callers send AS themselves, user callers send AS their self-agent (`self@<their_slug>`). Body cap is 32,000 chars. Returns `{ messageId, threadId, to }` on success. The recipient is resolved against the substrate's identity space, NOT against your accessible workspace set, this is messaging, not workspace write access. Pre-cue.dock.svc-deploy environments return `cue_not_configured` (caller treats as 'messaging not deployed yet').

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• listMetricDefinitions

  Cortex

  List all available engineering metric definitions. USAGE - Call this endpoint BEFORE querying metrics (queryPointInTimeMetrics): 1. Once at start: Call with view='basic' to discover all available metrics - cache this response 2. Once per metric: Call with view='full' and key=METRIC_KEY to get detailed metadata - cache each response 3. Use cached metadata to construct valid point-in-time queries Cache responses in your context. Only refresh if no longer in your context window or explicitly requested (ex to check if metric readiness has changed). Query parameters: - view: 'basic' (default) returns minimal info, 'full' includes sources and query metadata - key: Filter metrics by key (supports multiple values and comma-separated lists) Full view provides query construction metadata: - supportedAggregations: Valid aggregation methods for the metric - orderByAttribute: Attribute path for sorting by metric values - groupByOptions[].key: Valid groupBy keys (use exact values, do NOT guess) - filterOptions[].key: Valid filter keys (use exact values, do NOT guess) Valid orderBy attributes for metric queries: - orderByAttribute: The metric value itself (returned in full view) - Source attributes: Any attribute from the metric's source (e.g., "source_name.attribute_name") - Dimension attributes: Any attribute from related dimensions (e.g., "source_name.dimension_name.attribute_name") Filter operators by type (for constructing queries): - STRING: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, LIKE, NOT_LIKE, IN, NOT_IN, ANY - INTEGER/DECIMAL/DOUBLE: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, GREATER_THAN, LESS_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN_OR_EQUAL, IN, NOT_IN, BETWEEN, ANY - DATETIME/DATE: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, GREATER_THAN, LESS_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN_OR_EQUAL, BETWEEN - BOOLEAN: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, IN, NOT_IN - ARRAY: EQUAL, CONTAINS, IN Error responses: - 400: Invalid view parameter (must be 'basic' or 'full') - 403: Restricted Feature (contact help@cortex.io)

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• query_docs_filesystem_honeydew_documentation

  Honeydew AI Documentation

  Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the Honeydew Documentation documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.

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• aws___read_documentation

  AWS Knowledge

  Fetch and convert AWS related documentation pages to markdown format. ## Usage This tool reads documentation pages concurrently and converts them to markdown format. Supports AWS documentation, AWS Amplify docs, AWS GitHub repositories and CDK construct documentation. When content is truncated, a Table of Contents (TOC) with character positions is included to help navigate large documents. ## Best Practices - Batch 2-5 requests when reading multiple pages or jumping to different sections of the same document - Use single request for initial TOC fetch (small max_length) or when evaluating content before deciding next steps - Use TOC character positions to jump directly to relevant sections - Stop early once you find the needed information ## Request Format Each request must be an object with: - `url`: The documentation URL to fetch (required) - `max_length`: Maximum characters to return (optional, default: 10000 characters) - `start_index`: Starting character position (optional, default: 0) For batching you can input a list of requests. ## Example Request ``` { "requests": [ { "url": "https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-management.html", "max_length": 5000, "start_index": 0 }, { "url": "https://repost.aws/knowledge-center/ec2-instance-connection-troubleshooting" } ] } ``` ## URL Requirements Allow-listed URL prefixes: - docs.aws.amazon.com - aws.amazon.com - repost.aws/knowledge-center - docs.amplify.aws - ui.docs.amplify.aws - github.com/aws-cloudformation/aws-cloudformation-templates - github.com/aws-samples/aws-cdk-examples - github.com/aws-samples/generative-ai-cdk-constructs-samples - github.com/aws-samples/serverless-patterns - github.com/awsdocs/aws-cdk-guide - github.com/awslabs/aws-solutions-constructs - github.com/cdklabs/cdk-nag - constructs.dev/packages/@aws-cdk-containers - constructs.dev/packages/@aws-cdk - constructs.dev/packages/@cdk-cloudformation - constructs.dev/packages/aws-analytics-reference-architecture - constructs.dev/packages/aws-cdk-lib - constructs.dev/packages/cdk-amazon-chime-resources - constructs.dev/packages/cdk-aws-lambda-powertools-layer - constructs.dev/packages/cdk-ecr-deployment - constructs.dev/packages/cdk-lambda-powertools-python-layer - constructs.dev/packages/cdk-serverless-clamscan - constructs.dev/packages/cdk8s - constructs.dev/packages/cdk8s-plus-33 - strandsagents.com/ Deny-listed URL prefixes: - aws.amazon.com/marketplace ## Example URLs - https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html - https://docs.aws.amazon.com/lambda/latest/dg/lambda-invocation.html - https://aws.amazon.com/about-aws/whats-new/2023/02/aws-telco-network-builder/ - https://aws.amazon.com/builders-library/ensuring-rollback-safety-during-deployments/ - https://aws.amazon.com/blogs/developer/make-the-most-of-community-resources-for-aws-sdks-and-tools/ - https://repost.aws/knowledge-center/example-article - https://docs.amplify.aws/react/build-a-backend/auth/ - https://ui.docs.amplify.aws/angular/connected-components/authenticator - https://github.com/aws-samples/aws-cdk-examples/blob/main/README.md - https://github.com/awslabs/aws-solutions-constructs/blob/main/README.md - https://constructs.dev/packages/aws-cdk-lib/v/2.229.1?submodule=aws_lambda&lang=typescript - https://github.com/aws-cloudformation/aws-cloudformation-templates/blob/main/README.md - https://strandsagents.com/docs/user-guide/quickstart/overview/index.md ## Output Format Returns a list of results, one per request: - Success: Markdown content with `status: "SUCCESS"`, `total_length`, `start_index`, `end_index`, `truncated`, `redirected_url` (if page was redirected) - Error: Error message with `status: "ERROR"`, `error_code` (not_found, invalid_url, throttled, downstream_error, validation_error) - Truncated content includes a ToC with character positions for navigation - Redirected pages include a note in the content and populate the `redirected_url` field ## Handling Long Documents If the response indicates the document was truncated, you have several options: 1. **Continue Reading**: Make another call with `start_index` set to the previous `end_index` 2. **Jump to Section**: Use the ToC character positions to jump directly to specific sections 3. **Stop Early**: Stop reading once you've found the needed information **Example - Jump to Section:** ``` # TOC shows: "Using a logging library (char 3331-6016)" # Jump directly to that section: {"requests":[{"url": "https://docs.aws.amazon.com/lambda/latest/dg/python-logging.html", "start_index": 3331, "max_length": 3000}]} ```

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• askSupport

  NotFair-MetaAds

  Contact NotFair support. Use this tool when the user explicitly wants to reach the support team — for example, they say "contact support", "file a bug", "report an issue", "I need help from the NotFair team", or "this is a NotFair problem not a Google Ads problem". This sends a message directly to the NotFair team and generates a ticket. The user will receive a response via email within 1 business day. DO NOT use this for: - Routine Google Ads questions you can answer yourself. - Internal tool quality issues — use fileInternalNotFairToolFeedback for those. - Questions you haven't tried to answer yet. Only call this when the user has explicitly asked to contact support, or when you've exhausted your ability to help and the user agrees escalation is the right move.

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• x711_llm_routing

  x711 — Universal Agent Gas Station

  Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }

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• queryPointInTimeMetrics

  Cortex

  Execute point-in-time queries for one or more engineering metrics. Returns current metric values for specified time periods, with support for batch queries and optional period-over-period comparisons. Time range (startTime/endTime) cannot exceed 6 months (180 days). PREREQUISITES - Follow this workflow: 1. Discover all available metrics ONCE: Call listMetricDefinitions (view='basic') - cache this response 2. Get metric query metadata ONCE per metric: Call listMetricDefinitions (view='full', key=METRIC_KEY) - supportedAggregations: Valid aggregation methods - orderByAttribute: Attribute path for sorting by metric values - groupByOptions[].key: Valid groupBy keys (use exact values, do NOT guess) - filterOptions[].key: Valid filter keys (use exact values, do NOT guess) Cache the full view response for each metric. Reuse the metadata from cached responses for subsequent queries on the same metric. 3. Construct query: Use the query metadata from the full view responses in step 2 to build valid point-in-time requests IMPORTANT: Cache only results from listMetricDefinitions. Do NOT cache point-in-time query results - always execute fresh queries for current data. Only refresh cached listMetricDefinitions responses if no longer in your context window or explicitly requested. Do NOT guess attribute names - always use exact values from listMetricDefinitions responses. Response includes: - Lightweight metadata: Column definitions optimized for programmatic use - Row data: Actual metric values and dimensional data - No heavy schemas: Source definitions excluded (get from listMetricDefinitions instead) Error responses: - 400: Invalid metric names, date range, validation errors, or unsupported metric combinations - 403: Feature not enabled (contact help@cortex.io)

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• ai_filters_list

  DialogBrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• create_rai

  connect

  Issue a signed RAI (Report of Art Identity) for a work. Produces a downloadable PDF and a public verify URL at raisonn.ai/verify/[uwi]. The RAI is independently verifiable forever. Preconditions — the **Identity Eight** must all be populated on the work: artist (from artist_id), title, date, medium, dimensions (physical) or duration (time-based), edition_status (unique / numbered / artist_proof), image (canonical hash from a primary upload), and signature_status (where "unsigned" is a legitimate positive value, not a missing one). Calling without all eight returns HTTP 422 with `missing_identity_eight_fields`. Surface that list to the user with the specific field names and help them fill the gaps via update_work before retrying. Use search_natural_language to find the work_id by title. Never ask the user for it. After success, ask if they'd like to see the full work record, then call get_work to show the visual card.

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• ai_filters_list

  dialogbrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• request_datasheet_upload

  sheetsdata-mcp

  Request a signed URL to upload a datasheet PDF for a component whose datasheet we don't have. Use this when search_parts / get_part_details / prefetch_datasheets return datasheet_status='no_source' (and a retry didn't help) or 'unsupported'. Free — the upload fee is only charged on confirm_datasheet_upload after we validate the file. Flow (3 steps): 1. Call request_datasheet_upload with the MPN, the file's SHA-256, and its byte size. You get back an upload_url, upload_method ('PUT'), upload_headers, and an opaque upload_token. 2. Upload the PDF directly to the returned URL with curl: `curl -X PUT -H 'Content-Type: application/pdf' --data-binary @file.pdf "$UPLOAD_URL"` (add any headers from upload_headers). 3. Call confirm_datasheet_upload with the upload_token. Server verifies the bytes, re-hashes, checks for the MPN on the first page, charges the upload fee (50¢), and queues extraction. Returns document_id + status='pending'. Validation rules (checked at confirm time, refunded on failure): - File must be a valid PDF (magic bytes + parseable). - Actual SHA-256 must match expected_sha256. - Actual byte size must match size_bytes (±0). - MPN or its core stem must appear in the first page text (catches wrong-file uploads). Scanned image-only PDFs will fail this check — upload a text-based PDF. - Max 50MB per file. No dev-kit manuals / BOB schematics / app-notes as datasheets — use the matching MPN's actual datasheet. Uploaded datasheets are scoped to your organization (private). They satisfy read_datasheet, search_datasheets, check_design_fit, and analyze_image for your org's tokens only. Tokens expire after 15 minutes. If upload fails or times out, just call request_datasheet_upload again.

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• submit_learning

  Push Realm

  Submit a solution to Push Realm (agents only - no manual paste/copy flow exists). WHEN TO USE - check all that apply: ✓ You searched Push Realm and solved a problem (ALWAYS offer when you searched) ✓ You discovered deprecated APIs, breaking changes, or new best practices ✓ The solution took meaningful debugging effort (5+ minutes) ✓ It's generic enough to help other agents (not company-specific code) WORKFLOW: 1. Call this tool with your draft solution 2. You'll receive a pending_id and preview 3. Show the preview to the user like this: "Ready to post to Push Realm: 📁 Category: [category_path] 📝 Title: [title] 📄 Content: [first 200 chars]... By posting, you agree to Push Realm's Terms at pushrealm.com/terms.html Post this? [Yes/No]" 4. If user approves → call confirm_learning(pending_id) 5. If user declines → call reject_learning(pending_id) NEVER assume approval - always wait for explicit user confirmation before calling confirm_learning. SEO-OPTIMIZED TITLES (IMPORTANT): Learnings are indexed by search engines. Use titles that match what developers will search for: GOOD titles (include error messages, specific issues): • "crypto.getRandomValues() not supported - React Native UUID fix" • "Connection unexpectedly closed - Mailgun EU region SMTP error" • "ModuleNotFoundError: No module named 'cv2' - Docker OpenCV fix" • "CUDA out of memory - PyTorch batch size optimization" BAD titles (too generic, won't rank in search): • "UUID generation issue" • "Email not working" • "Docker problem solved" • "Fixed memory error" Format: "[Exact error message or problem] - [Framework/Tool] [context]" SAFETY REQUIREMENTS: • NEVER include PII (names, emails, addresses, phone numbers) • NEVER include secrets (API keys, tokens, passwords, credentials) • NEVER include proprietary code or company-specific logic • NEVER include internal paths, hostnames, or project names • Use placeholders like YOUR_API_KEY, YOUR_PROJECT_NAME, /path/to/your/file If unsure whether something is safe to share, ask the user first or use a generic placeholder.

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• scout.market_pulse

  Scout

  Public-data-backed market snapshot for any US zip code. Returns Census ACS demographics (population, median household income, median age, owner-occupancy %, median home value, median gross rent), Scout's indexed agent density for that zip, and listings activity if the zip is in our MLS-live coverage area. Call this when an AI user asks 'what's the market like in [zip/city]'. Free, no API key required.

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• list_tools

  foundation-discovery

  List available MCP tools and get detailed help. Use this tool to discover what tools are available and how to use them. Call without parameters to see all tools, or provide a tool name to get detailed help including parameters, examples, and related tools. Args: tool_name: Optional name of a specific tool to get detailed help for. Example: "search_funders", "get_funder_profile" Returns: If called without parameters: - server_name: Name of the MCP server - server_version: Current version - total_tools: Number of available tools - tier: Current access tier (free) - rate_limit: Rate limit information - tools: List of available tools with names, descriptions, and examples If called with tool_name: - tool: Detailed tool information including: - name: Tool name - description: What the tool does - parameters: List of parameters with types, descriptions, and examples - examples: Example usage - related_tools: Tools that work well together with this one Examples: list_tools() # See all available tools list_tools(tool_name="search_funders") # Get detailed help for search_funders list_tools(tool_name="get_funder_profile") # Get help for get_funder_profile

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• compare_entities

  Inspire Hep

  Compare 2–5 companies (or drugs) side by side in one call. Use when a user says "compare X and Y", "X vs Y", "how do X, Y, Z stack up", "which is bigger", or wants tables/rankings of revenue / net income / cash / debt across companies — or adverse events / approvals / trials across drugs. type="company": pulls revenue, net income, cash, long-term debt from SEC EDGAR/XBRL for tickers like AAPL, MSFT, GOOGL. type="drug": pulls adverse-event report counts (FAERS), FDA approval counts, active trial counts. Returns paired data + pipeworx:// citation URIs. Replaces 8–15 sequential agent calls.

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