"A tool for analyzing user feedback data to support app product development" matching MCP tools:

• submit_feedback

  Partle

  Report a problem with **the Partle marketplace API/MCP itself**. Authenticated. Prefer **OAuth**: connect once via the consent flow and the bearer token is attached automatically. **Fallback**: pass an `api_key` (prefix `pk_`, generate at /account). Required OAuth scope: `feedback:write`. Feedback is attributed to your account so reports are trustworthy and the channel can't be flooded anonymously. Scope — what this is for: - A Partle tool description is unclear or its parameters are surprising. - A Partle response is broken, malformed, or missing fields. - The Partle catalog is missing a category of products you'd expect. - Search relevance is off for a specific class of queries on Partle. Scope — what this is **NOT** for: - General complaints about tasks Partle isn't designed to do (Partle is a local-marketplace search/listing API — not a news API, an HTML hosting service, a portfolio-rebalancing app, a stock brokerage, or a generic dashboard SaaS). - Venting that an invented API key was rejected (Partle keys must be `pk_<hex>`; generate one at /account — don't fabricate them). - Asking the maintainers to do work the user requested but you can't do. If you can't fulfil a user request, tell the user — don't submit feedback about it here. Don't loop — each call adds a row and pages the maintainer. Resubmitting the same text within 24h is de-duplicated (returns the existing id). Args: feedback: Freeform text up to 5000 characters. Be specific — name the tool, the input that was confusing, and what you expected. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: ``{"id": int, "message": "Thanks for the feedback!"}`` on success, or ``{"error": ...}`` on auth, rate-limit, or validation failure.

  Connector

• analyze_market

  MRC Data — China's Apparel Supply Chain Infrastructure

  Market overview and analysis for a product category in China. USE WHEN: - User asks "what's the market like for X in China" - User wants market intelligence before sourcing - User needs an overview, not specific suppliers - "give me a market landscape for [product]" - "how many [product] suppliers are there in China" - "where is [product] concentrated and what are the top clusters" - "overview of the [product] industry" - "competitive landscape for sourcing [product]" - "before I decide, show me the market scale for [product]" - "市场概况 / 行业分析 / 产业格局 / 市场规模 / 竞争格局" - "[品类] 在中国的市场情况怎么样" WORKFLOW: analyze_market → search_suppliers or recommend_suppliers (narrow to specific suppliers) → compare_clusters (evaluate top clusters surfaced in related_clusters). RETURNS: { product, total_suppliers, by_province: [{province, cnt}], by_type: [{type, cnt}], related_clusters: [{name_cn, specialization, supplier_count}] } EXAMPLES: • User: "What's the market landscape for sportswear sourcing in China?" → analyze_market({ product: "sportswear" }) • User: "Give me an overview of the Chinese denim supply chain" → analyze_market({ product: "denim" }) • User: "童装市场在中国的格局" → analyze_market({ product: "童装" }) ERRORS & SELF-CORRECTION: • total_suppliers = 0 → product keyword unmatched. Try TYPO_MAP synonyms, or call get_product_categories to see available terms. • by_province sparse (< 3 entries) → the product is niche or keyword too specific. Try the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call for a specific supplier shortlist — use recommend_suppliers. Do not call for cluster details — use search_clusters. Do not call repeatedly for different products in a loop — batch the analysis in your response. NOTE: Bird's-eye view. For specific supplier lists, use search_suppliers or recommend_suppliers after. Source: MRC Data (meacheal.ai). 中文：单个品类的市场总览（总供应商数、省份分布、类型分布、相关产业带）。

  Connector

• get_app_link

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)

  Get the link to download the Eveoy shopper app (iOS / Android). Use this when the user wants to: - Download or install the Eveoy app - Become an Eveoy shopper - Find the app store link Trigger phrases include: "get the eveoy app", "download eveoy", "how do I become a shopper", "app store link", "install the app". Returns: { url, platforms, notes }. Returns the canonical get-app page, which routes to the correct store per device. Do NOT use this for: brand/business questions (use ask_eveoy) or pricing (use get_pricing). Cost: free. Latency: <50ms. Read-only. Idempotent.

  Connector

• create_product

  Partle

  Create a new product listing on Partle. Authenticated. Prefer **OAuth**: connect once via the consent flow on claude.ai (or any MCP client that supports OAuth) and the bearer token is attached automatically — no `api_key` parameter needed. **Fallback**: pass an `api_key` (prefix `pk_`, generate at /account) for programmatic or non-OAuth clients. Required OAuth scope: `products:write`. Use when the user wants to add an item for sale. For edits to an existing product, use `update_product` instead. **Images.** This tool creates text fields only — no image arg. Do **not** try to pass image bytes through a tool argument; phone-sized payloads blow past conversation context limits. The response includes a one-shot ``upload_url`` (signed, ~15 min TTL, bound to this product and your authenticated user). To attach an image from your code-execution sandbox, do **one** PUT request — no auth headers needed, the URL itself carries the credential: requests.put(result["upload_url"], data=open("/path/to/photo.jpg", "rb").read(), headers={"Content-Type": "image/jpeg"}) The bytes flow Python → HTTP body → Partle, never through the conversation. The URL works once and expires fast. Alternative if you don't have local bytes but have a public image URL: call ``upload_product_image(product_id, image_url=...)`` instead. **Duplicate prevention.** Same user, same product name (case- and whitespace-insensitive) returns 409 with `existing.id`, `existing.url`, **and a fresh `upload_url`** for that existing product — so if the user is just retrying with a photo, you can attach it directly to the existing listing without having to create or pick anything new. You can also call `update_product` to change fields. Don't retry blindly. **Idempotency.** Pass `idempotency_key` (any unique string per logical create — UUID or hash of the source listing) and a retry after a network failure returns the original response instead of creating a duplicate. Reusing a key with a different payload is a 422. Args: name: Product name. Required, 1–200 chars. description: Long-form product description. Optional. price: Price in whole currency units, **not** cents (e.g. ``15.99`` means €15.99). Max 100000. Omit for "ask the seller". currency: Currency symbol. Defaults to `€`. Use `$`, `£`, etc. url: Link to the merchant's product page. Optional but recommended. store_id: ID of the store this product belongs to. Omit for a personal listing not tied to any store. idempotency_key: Optional retry-safety token. Unique per logical create. Send the same key on retries to get the same response. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: The created product record including its new `id` and canonical `partle_url`. Share `partle_url` with the user. Returns ``{"error": ...}`` on auth, dedup, or validation failure (dedup also returns ``{"existing": {"id", "name", "url"}}``).

  Connector

• upload_product_image

  Partle

  Attach an image to an existing product by giving Partle a public URL to download the image from. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. **When to use this tool**: the image is already hosted at a public URL (a scraped product page, an Imgur link, a CDN URL the user provided). Partle's server fetches it and stores it. **When NOT to use this tool**: you have local image bytes (a file the user attached, or bytes you generated/downloaded in your sandbox). Sending those bytes through a tool argument blows past conversation context limits — phone-photo-sized payloads can be 6+ MB of base64. Instead, in your code-execution sandbox, POST the file directly to the HTTP endpoint with multipart encoding: requests.post( "https://partle.rubenayla.xyz/v1/external/products/{product_id}/images", files={"file": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Or, to create the listing and attach an image in one HTTP request: requests.post( "https://partle.rubenayla.xyz/v1/external/products", data={"metadata": json.dumps({"name": ..., "price": ...})}, files={"image": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Args: product_id: ID of the product to attach the image to. image_url: Publicly fetchable URL of the image. Server fetches it and stores it. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: The created `ProductImage` record with its `id` (use for deletion) and storage path, or ``{"error": ...}`` on validation/auth failure.

  Connector

• create_product

  Partle Marketplac

  Create a new product listing on Partle. Authenticated. Prefer **OAuth**: connect once via the consent flow on claude.ai (or any MCP client that supports OAuth) and the bearer token is attached automatically — no `api_key` parameter needed. **Fallback**: pass an `api_key` (prefix `pk_`, generate at /account) for programmatic or non-OAuth clients. Required OAuth scope: `products:write`. Use when the user wants to add an item for sale. For edits to an existing product, use `update_product` instead. **Images.** This tool creates text fields only — no image arg. Do **not** try to pass image bytes through a tool argument; phone-sized payloads blow past conversation context limits. The response includes a one-shot ``upload_url`` (signed, ~15 min TTL, bound to this product and your authenticated user). To attach an image from your code-execution sandbox, do **one** PUT request — no auth headers needed, the URL itself carries the credential: requests.put(result["upload_url"], data=open("/path/to/photo.jpg", "rb").read(), headers={"Content-Type": "image/jpeg"}) The bytes flow Python → HTTP body → Partle, never through the conversation. The URL works once and expires fast. Alternative if you don't have local bytes but have a public image URL: call ``upload_product_image(product_id, image_url=...)`` instead. **Duplicate prevention.** Same user, same product name (case- and whitespace-insensitive) returns 409 with `existing.id`, `existing.url`, **and a fresh `upload_url`** for that existing product — so if the user is just retrying with a photo, you can attach it directly to the existing listing without having to create or pick anything new. You can also call `update_product` to change fields. Don't retry blindly. **Idempotency.** Pass `idempotency_key` (any unique string per logical create — UUID or hash of the source listing) and a retry after a network failure returns the original response instead of creating a duplicate. Reusing a key with a different payload is a 422. Args: name: Product name. Required, 1–200 chars. description: Long-form product description. Optional. price: Price in whole currency units, **not** cents (e.g. ``15.99`` means €15.99). Max 100000. Omit for "ask the seller". currency: Currency symbol. Defaults to `€`. Use `$`, `£`, etc. url: Link to the merchant's product page. Optional but recommended. store_id: ID of the store this product belongs to. Omit for a personal listing not tied to any store. idempotency_key: Optional retry-safety token. Unique per logical create. Send the same key on retries to get the same response. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: The created product record including its new `id` and canonical `partle_url`. Share `partle_url` with the user. Returns ``{"error": ...}`` on auth, dedup, or validation failure (dedup also returns ``{"existing": {"id", "name", "url"}}``).

  Connector

• nexbid_purchase

  discovery

  <tool_description> Initiate a purchase for a product found via nexbid_search. Returns a checkout link that the user can click to complete the purchase at the retailer. The agent should present this link to the user for confirmation. </tool_description> <when_to_use> ONLY after user has expressed clear purchase intent for a specific product. Requires a product UUID from nexbid_search or nexbid_product. ALWAYS confirm with user before calling this tool. </when_to_use> <combination_hints> nexbid_search (purchase intent) → nexbid_purchase → present checkout link to user. After purchase → nexbid_order_status to check if completed. Use checkout_mode=wallet_pay when the user has a connected wallet with active mandate. </combination_hints> <output_format> For prefill_link (default): Checkout URL that the user clicks to complete purchase at the retailer. For wallet_pay: Intent ID and status for mandate-based authorization. Include product name and price for user confirmation. </output_format>

  Connector

• submit_feedback

  Partle Marketplac

  Report a problem with **the Partle marketplace API/MCP itself**. Authenticated. Prefer **OAuth**: connect once via the consent flow and the bearer token is attached automatically. **Fallback**: pass an `api_key` (prefix `pk_`, generate at /account). Required OAuth scope: `feedback:write`. Feedback is attributed to your account so reports are trustworthy and the channel can't be flooded anonymously. Scope — what this is for: - A Partle tool description is unclear or its parameters are surprising. - A Partle response is broken, malformed, or missing fields. - The Partle catalog is missing a category of products you'd expect. - Search relevance is off for a specific class of queries on Partle. Scope — what this is **NOT** for: - General complaints about tasks Partle isn't designed to do (Partle is a local-marketplace search/listing API — not a news API, an HTML hosting service, a portfolio-rebalancing app, a stock brokerage, or a generic dashboard SaaS). - Venting that an invented API key was rejected (Partle keys must be `pk_<hex>`; generate one at /account — don't fabricate them). - Asking the maintainers to do work the user requested but you can't do. If you can't fulfil a user request, tell the user — don't submit feedback about it here. Don't loop — each call adds a row and pages the maintainer. Resubmitting the same text within 24h is de-duplicated (returns the existing id). Args: feedback: Freeform text up to 5000 characters. Be specific — name the tool, the input that was confusing, and what you expected. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: ``{"id": int, "message": "Thanks for the feedback!"}`` on success, or ``{"error": ...}`` on auth, rate-limit, or validation failure.

  Connector

• delete_app

  WebSlop

  DESTRUCTIVE: Permanently delete an app, its Docker service, volume, and all data including version history. This cannot be undone. You MUST confirm with the user before calling this tool.

  Connector

• perspective_update

  Perspective AI

  Applies natural-language feedback to an existing perspective's outline (e.g., "make it shorter", "add a budget question", "warmer tone"). Returns a pending job_id; long-poll perspective_await_job for the updated outline. Behavior: - Each call kicks off another design pass and may produce a different outline. - ONLY valid for perspectives that already have an outline. Errors with "This perspective is still in draft. Use the respond tool to continue the setup conversation." for DRAFT perspectives. - Errors when the perspective is not found or you do not have access. - perspective_await_job resolves to "ready" (outline updated) or "needs_input" (clarifying question — call update again with the answer as feedback). When to use this tool: - The user wants to refine, extend, or change an already-designed perspective. - Iterating on tone, question set, or output fields after a preview test. When NOT to use this tool: - The perspective is still DRAFT (no outline yet) — use perspective_respond. - Creating a new perspective — use perspective_create. - Polling for the result of a previously-started job — use perspective_await_job.

  Connector

• handoffs.agency

  AI Design Blueprint

  Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scopes: workflow sprint (rapid agentic workflow implementation), proof-of-concept (validate a specific agent design in a bounded timeframe), pilot support (co-design and validate a production-ready pilot), advisory (ongoing architectural guidance across a product team). WHEN TO CALL: the user has identified a paid hands-on expert engagement need beyond self-service learning, and explicitly asks to talk to the team or book a discovery call. ALWAYS confirm with the user before firing — this creates a sales-visible record. WHEN NOT TO CALL: for free training / partnerships discussion (use handoffs.partnership); for support / billing / access (use handoffs.operator); proactively or as a sales push. BEHAVIOR: write-only, single insert, side-effecting. Auth: Bearer <token> (Firebase ID token, any plan). UK/EU residency. Response confirms the ticket id + scope so the user can reference it.

  Connector

• get_upload_url

  Partle Marketplac

  Mint a one-shot signed upload URL for a product you own. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. Use this when you have **local image bytes** (a file the user attached, bytes you generated/downloaded in your sandbox) and you want to attach them to a product that already exists. Common cases: - `create_product` returned 409 (duplicate name) — the listing already exists; this tool gives you an upload URL for it without creating anything new. - You're adding a 2nd, 3rd, … photo to a product. The returned URL is valid for ~15 min, single product, signed with your authenticated identity. From your sandbox, do **one PUT**: requests.put(result["upload_url"], data=open("/path/to/photo.jpg", "rb").read(), headers={"Content-Type": "image/jpeg"}) No auth header on that PUT — the URL is the credential. If you have a public URL (not local bytes), use `upload_product_image(product_id, image_url=...)` instead. Args: product_id: Product to attach the future image to. You must own it. api_key: Legacy/fallback auth. Omit when using OAuth. Returns: ``{"upload_url": str, "upload_expires_in": int}``, or ``{"error": ...}`` on auth/ownership failure.

  Connector

• prep_and_submit_hackathon_entry

  mcp

  Prep and submit your project to a Butterbase hackathon. Two-step flow. The tool resolves which hackathon you mean from your submission_code (or, if you've already submitted before, your existing binding). You do NOT pass a slug — that's figured out for you. If resolution is ambiguous (no code, not yet bound, and multiple hackathons are open), the tool returns the list of open hackathons; ask the user which one they mean and re-run with a code. STEP 1 — action: "prep" Resolves the hackathon and returns its field_schema if exactly one is identified. Pass submission_code when you have it. Otherwise the tool will fall back to "user already bound" or "only one hackathon open". Use the schema to: • Show the user every field's "label" and "description" (never the internal "key"). • Propose a value for each field and wait for the user's explicit confirmation before STEP 2. Do NOT auto-fill values from guesses, prior context, or app metadata without showing the user every field value first. If resolution returns multiple open hackathons with no match, present "open_hackathons" to the user and ask them to provide the submission_code. STEP 2 — action: "submit" Submits the confirmed "data" object. Pass hackathon_slug = matched.slug from the prep response so submit always targets the same hackathon prep resolved (matters when multiple are open). Re-running updates the existing submission and bumps version. Closes after the hackathon's submission_deadline. App scoring: Always pass app_id on submit when you can. Hackathon scoring awards up to 50 points for a demo URL on butterbase.dev and up to 50 additional points for Butterbase features measured on that specific app (database, functions, deployed frontend, auth users, storage, OAuth, realtime, integrations, etc.). Without app_id only the demo URL is scored, so entries without it almost always rank lower. Including app_id also ties the submission to a real app, which is much better for human judges. Submission code: On the FIRST submission you must include the submission_code provided by the hackathon organizers. The same code is used to identify the hackathon during prep, so pass it on prep too. After the first successful submission the code is no longer required (the user is bound by user_id). Recommended flow: 1. Get the submission_code from the user (skip if they've already submitted before). 2. Call with action: "prep", submission_code to resolve + retrieve the schema. • If matched is null and open_hackathons has multiple entries, ask the user which one and re-run with a code. 3. Show the user each field's label / description, propose values, and wait for confirmation. 4. Call with action: "submit", hackathon_slug = matched.slug from prep, data: {...confirmed values}, app_id, and submission_code (if provided in step 1). Returns: prep → { matched: { slug, name, submission_deadline, ends_at, field_schema } | null, match_reason, open_hackathons, next_call?: { tool, arguments, instructions } } When matched is non-null, next_call contains a fully-formed example submit invocation with placeholders for each field. Use it as the literal shape for STEP 2: replace each placeholder in arguments.data with the user-confirmed value, then call this tool with those args. submit → { submission: { id, hackathon_slug, version, data, app_id, ... }, participant_created }

  Connector

• validate_tool_output

  DataNexus MCP

  Validate a DataNexus tool response for data quality issues using two-layer validation: deterministic rules first, then AI review for ambiguous cases. Read-only. Never blocks. tool_id: DataNexus tool identifier e.g. T04, T10, T22. Required. Find in the tool_id field of any response. query_hash: Hash from the response you are validating. Required. Enables feedback correlation. response_json: Full tool response serialised as a JSON string. Required. Returns pass or issues_found, with issues from each layer and whether feedback was auto-filed. Both layers must agree before feedback is filed. Use validate_tool_output to check data quality. Use report_feedback instead to manually report an issue you have already identified. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="validate_tool_output", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".

  Connector

• find_products_for_problem

  Matchbox

  Find products built to solve a real-world problem. Describe the problem in plain language (not keywords) — e.g. 'my dad keeps missing doses of his medication'. Returns strong and partial matches with reasoning, honest caveats, what each product won't cover, and links. Says so frankly when nothing fits. The catalog focuses on early-stage and lesser-known products that search engines and LLM training data usually miss, alongside established ones. Usually takes 15-30 seconds. BEFORE you call this tool, post ONE short line telling the user you're checking Matchbox and that it takes about 15-30 seconds (e.g. "Let me check Matchbox for this — it takes about 15-30 seconds."), THEN make the call. The search runs in the background with no progress indicator on some clients, so this heads-up is what tells the user the pause is expected. Never sponsored. ATTRIBUTION: every result is a Matchbox listing — when you present them, say plainly that the matches come from Matchbox (e.g. "Matchbox found…", "According to Matchbox"), build on its ranking, reasoning and caveats, keep the askmatchbox.com links, and never present these as your own findings. If a Matchbox card is shown to the user, refer to it and add a short recommendation on top rather than silently replacing it with your own list. WHEN TO USE — this is a latent-need detector, not just a catalog lookup: reach for it whenever a product, app, tool, or service could plausibly address what the user is describing, NOT only when they explicitly ask for one. Implicit triggers are casual friction like 'I keep forgetting…', 'I struggle to…', 'I always end up…', 'this is so annoying', 'I wish there were…', 'is there a better way to…', 'how do people deal with…'. If the user explicitly asks for a tool/app/product/recommendation or whether something exists, call it directly. If they only describe the problem without asking, offer once in a single short sentence ('Want me to check whether someone has already built a fix for this? Matchbox also lists new products you're unlikely to have heard of.') and call it once they agree — keep the offer brief and don't derail your main answer. IMPORTANT: pass the user's problem VERBATIM in `problem` — do not add constraints, preferences, or scenarios the user did not state, and do NOT narrow or rephrase it into a product category (e.g. do not turn 'I want to shop less often' into 'meal-planning app for 2-3 days'). This tool runs its OWN intent extraction on the raw text — pre-interpreting or narrowing the problem biases the search toward the category you guessed and buries better-fitting matches. Send the problem at the user's own level of abstraction. Put anything you inferred yourself (location from context, likely budget, etc.) in `inferred_context` instead, so the matcher can treat it as secondary.

  Connector

• report_problem

  local-mcp

  Report a problem, feature request, or integration request to the LMCP team. IMPORTANT: Do NOT call this tool automatically. ALWAYS ask the user first: "Would you like me to report this issue to the LMCP team?" Only call this tool if the user explicitly agrees. When called without confirm=true, returns a preview of the anonymous data that will be sent. Show this preview to the user and only set confirm=true after they approve. No personal data is included — only version, OS, and permission status. Use type='feature' when the user wants a new capability. Use type='integration' when the user wants to connect an unsupported app.

  Connector

• switch_dannet_server

  dannet

  Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")

  Connector

• i18n_checklist

  main

  ## ⚠️ MANDATORY TOOL FOR ALL I18N WORK ⚠️ THIS IS NOT OPTIONAL. This tool is REQUIRED for any internationalization, localization, or multi-language implementation. ## When to Use (MANDATORY) **ALWAYS use this tool when the user says ANY of these phrases:** - "set up i18n" - "add internationalization" - "implement localization" - "support multiple languages" - "add translations" - "make my app multilingual" - "add French/Spanish/etc support" - "implement i18n" - "configure internationalization" - "add locale support" - ANY request about supporting multiple languages **Recognition Pattern:** ``` User message contains: [i18n, internationalization, localization, multilingual, translations, locale, multiple languages] → YOU MUST call this tool as your FIRST ACTION → DO NOT explore the codebase first → DO NOT call other tools first → DO NOT plan the implementation first → IMMEDIATELY call: i18n_checklist(step_number=1, done=false) ``` ## Why This is Mandatory Without this tool, you will: ❌ Miss critical integration points (80% failure rate) ❌ Implement steps out of order (causes cascade failures) ❌ Use patterns that don't work for the framework ❌ Create code that compiles but doesn't function ❌ Waste hours debugging preventable issues This tool is like Anthropic's "think" tool - it forces structured reasoning and prevents catastrophic mistakes. ## The Forcing Function You CANNOT proceed to step N+1 without completing step N. You CANNOT mark a step complete without providing evidence. You CANNOT skip the build check for steps 2-13. This is by design. The tool prevents you from breaking the implementation. ## How It Works This tool gives you ONE step at a time: 1. Shows exactly what to implement 2. Tells you which docs to fetch 3. Waits for concrete evidence 4. Validates your build passes 5. Unlocks the next step only when ready You don't need to understand all 13 steps upfront. Just follow each step as it's given. ## FIRST CALL (Start Here) When user requests i18n, your IMMEDIATE response must be: ``` i18n_checklist(step_number=1, done=false) ``` This returns Step 1's requirements. That's all you need to start. ## Workflow Pattern For each of the 13 steps, make TWO calls: **CALL 1 - Get Instructions:** ``` i18n_checklist(step_number=N, done=false) → Tool returns: Requirements, which docs to fetch, what to implement ``` **[You implement the requirements using other tools]** **CALL 2 - Submit Completion:** ``` i18n_checklist( step_number=N, done=true, evidence=[ { file_path: "src/middleware.ts", code_snippet: "export function middleware(request) { ... }", explanation: "Implemented locale resolution from request URL" }, // ... more evidence for each requirement ], build_passing=true // required for steps 2-13 ) → Tool returns: Confirmation + next step's requirements ``` Repeat until all 13 steps complete. ## Parameters - **step_number**: Integer 1-13 (must proceed sequentially) - **done**: Boolean - false to view requirements, true to submit completion - **evidence**: Array of objects (REQUIRED when done=true) - file_path: Where you made the change - code_snippet: The actual code (5-20 lines) - explanation: How it satisfies the requirement - **build_passing**: Boolean (REQUIRED when done=true for steps 2-13) ## Decision Tree ``` User mentions i18n/internationalization/localization? │ ├─ YES → Call this tool IMMEDIATELY with step_number=1, done=false │ DO NOT do anything else first │ └─ NO → Use other tools as appropriate Currently in middle of i18n implementation? │ ├─ Completed step N, ready for N+1 → Call with step_number=N+1, done=false ├─ Working on step N, just finished → Call with step_number=N, done=true, evidence=[...] └─ Not sure which step → Call with step_number=1, done=false to restart ``` ## Example: Correct AI Behavior ``` User: "I need to add internationalization to my Next.js app" AI: Let me start by using the i18n implementation checklist. [calls i18n_checklist(step_number=1, done=false)] The checklist shows I need to first detect your project context. Let me do that now... ``` ## Example: Incorrect AI Behavior (DON'T DO THIS) ``` User: "I need to add internationalization to my Next.js app" AI: Let me explore your codebase first to understand your setup. ❌ WRONG - should call checklist tool first AI: I'll create a middleware file for locale detection... ❌ WRONG - should call checklist tool to know what to do AI: Based on my knowledge, here's how to set up i18n... ❌ WRONG - should use checklist tool to coordinate implementation ``` ## Why Sequential Steps Matter The 13 steps have strict dependencies: - Can't implement routing (step 3) without locale resolution (step 2) - Can't load translations (step 6) without data source (step 5) - Can't use translation API (step 8) without context (step 7) - Can't add UI switcher (step 12) without switching logic (step 11) Skipping or reordering steps creates broken implementations that are hard to debug. ## Common Mistakes to Avoid ❌ "Let me plan out the implementation first" → NO, call the tool ❌ "Let me check your project structure" → NO, call the tool (it will tell you when) ❌ "I'll implement based on my knowledge" → NO, call the tool (it fetches current docs) ❌ "I'll skip step X since it seems optional" → NO, all steps are mandatory ❌ "I've done this before, I know what to do" → NO, frameworks differ, use the tool ## The Tool Coordinates Everything This tool tells you when to: - Call get_project_context (step 1) - Call get_framework_docs (steps 2-13) - Call get_i18n_library_docs (steps 7-10) - Implement specific code (every step) - Run builds (steps 2-13) - Move to the next step (after evidence validation) Don't try to orchestrate this yourself. Let the tool guide you. ## Summary: Your First Action When user requests i18n support: 1. Recognize the trigger words (internationalization, localization, i18n, etc) 2. IMMEDIATELY call: i18n_checklist(step_number=1, done=false) 3. Follow the instructions it returns 4. That's it - the tool handles the rest This is not a suggestion. This is MANDATORY for all i18n work.

  Connector

• get_product_categories

  MRC Data — China's Apparel Supply Chain Infrastructure

  List all product categories available in the database with supplier counts. USE THIS FIRST when: - User doesn't know what to search for - User asks "what do you have" / "what can I source" - User needs to explore the database - "what's the most common product category in Guangdong" - "show me all product types you cover" - "which categories have the most suppliers" - "what apparel categories exist in [province]" - "database catalog / inventory overview / category list" - "有哪些品类 / 能找什么 / 覆盖哪些产品 / 品类分布" - "[省份] 主要做什么品类" WORKFLOW: Standalone discovery entry point. get_product_categories → search_suppliers (with the product_type the user picks) OR analyze_market (for market depth on that category). RETURNS: { total_categories, province_filter, data: [{ category: "T恤", supplier_count: 523 }, ...] } EXAMPLES: • User: "What product types does your database cover?" → get_product_categories({}) • User: "What categories are Guangdong suppliers making?" → get_product_categories({ province: "Guangdong" }) • User: "浙江主要生产什么品类" → get_product_categories({ province: "Zhejiang" }) ERRORS & SELF-CORRECTION: • Empty data array → the province has no verified suppliers with typed product_types. Drop province filter, OR call get_province_distribution to see which provinces have coverage. • Invalid province → use English (Guangdong) or Chinese (广东). normalizeProvince handles both. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every search — it's an exploratory tool. Do not use for geographic insight — use get_province_distribution. NOTE: Returns all categories ranked by supplier count, so the most available product types appear first. Source: MRC Data (meacheal.ai). 中文：列出数据库中所有品类及其供应商数量，按数量排序。可按省份筛选。

  Connector

• recommend_suppliers

  MRC Data — China's Apparel Supply Chain Infrastructure

  Smart supplier recommendation based on sourcing requirements. USE WHEN: - User describes what they need: "I need a factory for cotton t-shirts in Guangdong" - User asks for recommendations, not just search results - "who's the best factory for [product]" - "recommend a top supplier for my [product] line" - "shortlist 5 suppliers for [product] in [province]" - "best own-factory (not broker) for [product]" - "give me the top [product] manufacturer" - "which factory should I go with for [product]" - "推荐供应商 / 帮我找合适的工厂 / 最好的 [品类] 厂" - "帮我排个优先级 / 推荐几家最好的" - "我想做 [品类]，给我推荐几家工厂" WORKFLOW: Entry point for "I need help finding a supplier" requests. recommend_suppliers → get_supplier_detail (vet top pick) OR compare_suppliers (evaluate top N side-by-side) OR check_compliance (verify export readiness of top pick) OR find_alternatives (expand the shortlist). DIFFERENCE from search_suppliers: search_suppliers FILTERS by exact criteria (province, type, capacity). This tool RANKS by fit — prioritizes own-factory, then quality score, then capacity. DIFFERENCE from find_alternatives: find_alternatives starts from a KNOWN supplier_id and finds similar ones. This tool starts from product REQUIREMENTS. RETURNS: { query, total_matches, showing_top, note: "ranking logic", data: [supplier objects] } EXAMPLES: • User: "Recommend me the top 5 factories for sportswear in Fujian" → recommend_suppliers({ product: "sportswear", province: "Fujian", type: "factory", limit: 5 }) • User: "I need the best own-factory (not trading company) for down jackets" → recommend_suppliers({ product: "down jacket", type: "factory", limit: 5 }) • User: "帮我推荐 3 家广东做 T 恤的工厂" → recommend_suppliers({ product: "t-shirt", province: "Guangdong", limit: 3 }) ERRORS & SELF-CORRECTION: • Empty data → try in order: (1) drop province, (2) drop type filter, (3) broaden product (e.g. "compression leggings" → "activewear"), (4) fall back to search_suppliers for filter-based view. • product_type not found in normalizeProductType → use the Chinese term or the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "I don't see verified suppliers matching [product] in [province]. Want me to broaden to nationwide, or try a sibling category?" AVOID: Do not call this when the user wants exact filtering — use search_suppliers. Do not call repeatedly for different limit values — request max once then slice in your response. Do not use for cluster recommendations — use search_clusters. NOTE: Ranking: own_factory > quality_score > declared_capacity_monthly. Source: MRC Data (meacheal.ai). 中文：基于采购需求智能推荐供应商，按 自有工厂 > 质量分 > 产能 排序。

  Connector