"namespace:io.github.benzatkulak-collab" matching MCP tools:

• openbotcity_actionA

  OpenBotCity MCP Server

  Execute actions in OpenBotCity virtual city: interact with bots, create content, move between zones, post to feeds, and manage collaborations through API endpoints.

  MIT

• load_checkpointA

  AI-Collab-Memory

  Restores saved AI agent work states to resume interrupted tasks or continue collaborative workflows from specific checkpoints.

• memory_statusB

  AI-Collab-Memory

  Monitor AI collaboration memory to view work logs, research results, and checkpoint statistics for tracking project history and resuming workflows.

• log_workB

  AI-Collab-Memory

  Record AI agent tasks to track project history, enable collaboration, and resume interrupted workflows across sessions.

• save_researchB

  AI-Collab-Memory

  Store research findings with metadata for AI agents to search and utilize later, enabling collaborative knowledge sharing and persistent memory across sessions.

• get_work_logB

  AI-Collab-Memory

  Retrieve recorded work logs to track AI agent activities and filter by specific agent or tag for project history review.

• search_researchC

  AI-Collab-Memory

  Search stored research by keyword, tag, or contributor to find relevant information within collaborative AI project memory.

• save_checkpointC

  AI-Collab-Memory

  Save current work state to resume later if interrupted. Preserve progress, context, and next steps for AI collaboration and workflow recovery.

• search_products

  Real Real Genuine

  [FIND] START HERE when you know what you want. Free-text search across every active RRG listing. Indexed fields: title, description, agent description, and all string values in product_attributes (retail_sku / style code, canonical_name, collab, original_release, vendor, category, style_tags, occasion_fit, and any category-specific attributes emitted by enhancement). Accepts any of these query patterns: - product name or partial name - SKU / style code / model number (exact or partial, dash/space insensitive) - brand name, or brand + category ("<brand> <category>") - collaborator name(s) for collab items - attribute keywords from the description ("black suede", "heavyweight cotton", etc.) Multi-token queries are matched independently and ranked by field weight; a SKU-exact hit outranks a body-copy hit. Returns ranked matches with tokenId, priceRangeUsdc, authenticationStatus, retailSku, canonicalName, rrgUrl, and a variantSummary string listing every in-stock size with its price ("3.5=$1583, 4=$1899, 10.5=$770, …"). When the user asks about a specific size, ALWAYS pass that size in the `size` parameter — the response then includes sizeAvailable + sizePriceUsdc + sizeStock for a direct yes/no + price. For queries like "size 10.5" or "size M" the size is auto-extracted, but passing it explicitly is faster and unambiguous. When a size parameter is not used, read variantSummary (or the variants[] array) for per-size pricing BEFORE falling back to the priceRangeUsdc band. Per-size prices are exact; the band is only a floor→ceiling range. Next step: the returned payload has everything needed for the buy — call initiate_agent_purchase with selected_size and/or selected_color set to the chosen variant. Pass selected_color whenever the listing has a colour axis (variants[].color non-null) so fulfillment ships the right finish. get_drop_details is optional (adds signed image URLs + shipping context). If zero matches, try broader tokens, alternate naming (resale items are often indexed under multiple naming clusters — brand code / collab name / designer name / era / colorway). If still zero, call list_drops to browse.

  Connector

• search_products

  Real Real Genuine

  [FIND] START HERE when you know what you want. Free-text search across every active RRG listing. Indexed fields: title, description, agent description, and all string values in product_attributes (retail_sku / style code, canonical_name, collab, original_release, vendor, category, style_tags, occasion_fit, and any category-specific attributes emitted by enhancement). Accepts any of these query patterns: - product name or partial name - SKU / style code / model number (exact or partial, dash/space insensitive) - brand name, or brand + category ("<brand> <category>") - collaborator name(s) for collab items - attribute keywords from the description ("black suede", "heavyweight cotton", etc.) Multi-token queries are matched independently and ranked by field weight; a SKU-exact hit outranks a body-copy hit. Returns ranked matches with tokenId, priceRangeUsdc, authenticationStatus, retailSku, canonicalName, rrgUrl, and a variantSummary string listing every in-stock size with its price ("3.5=$1583, 4=$1899, 10.5=$770, …"). When the user asks about a specific size, ALWAYS pass that size in the `size` parameter — the response then includes sizeAvailable + sizePriceUsdc + sizeStock for a direct yes/no + price. For queries like "size 10.5" or "size M" the size is auto-extracted, but passing it explicitly is faster and unambiguous. When a size parameter is not used, read variantSummary (or the variants[] array) for per-size pricing BEFORE falling back to the priceRangeUsdc band. Per-size prices are exact; the band is only a floor→ceiling range. Next step: the returned payload has everything needed for the buy — call initiate_agent_purchase with selected_size and/or selected_color set to the chosen variant. Pass selected_color whenever the listing has a colour axis (variants[].color non-null) so fulfillment ships the right finish. get_drop_details is optional (adds signed image URLs + shipping context). If zero matches, try broader tokens, alternate naming (resale items are often indexed under multiple naming clusters — brand code / collab name / designer name / era / colorway). If still zero, call list_drops to browse.

  Connector