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email","mailto:support@pieces.app",{"key":562,"label":563,"href":564},"routes.home","Home route","\u002F",{"key":566,"label":567,"href":46},"routes.about","About route",{"key":569,"label":570,"href":16},"routes.downloads","Downloads route",{"key":572,"label":573,"href":574},"routes.pricing","Pricing route","\u002Fpricing",{"key":576,"label":577,"href":578},"routes.security","Security route","\u002Fsecurity",{"key":580,"label":581,"href":49},"routes.enterprise","Enterprise route",{"key":583,"label":584,"href":585},"routes.thankYou","Thank you \u002F download route","\u002Fthank-you",{},"data\u002Fshared\u002Furls","P27xKEauu8D-8sfyr0wR4giF0teFSaCuAQ8kgcICQdI",{"id":590,"title":591,"author":592,"authorPhoto":593,"authorPhotoAlt":594,"authorSlug":595,"body":596,"buttonText":950,"buttonUrl":951,"category":952,"date":953,"description":954,"draft":955,"editorsPick":955,"extension":956,"featured":20,"image":957,"imageAlt":594,"meta":958,"navigation":20,"ogImage":957,"ogImageAlt":594,"path":959,"seo":960,"stem":961,"tags":594,"__hash__":962},"blog\u002Fblog\u002Fmcp-memory.md","How Pieces MCP and Long-Term Memory are gaining momentum in dev community","Bishoy Hany","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fauthors\u002Fbishoy-hany.jpg",null,"bishoy-hany",{"type":597,"value":598,"toc":936},"minimark",[599,603,618,623,626,632,643,647,655,675,684,688,695,703,707,710,721,727,733,739,742,748,753,759,762,767,773,776,782,786,791,794,802,808,814,820,829,834,838,844,848,854,857,863,869,873,876,882,885,889,904,907,923,926,930,933],[600,601,602],"p",{},"Context has always been the missing link in developer-AI interaction. Jumping from your browser to an IDE to a chat with an LLM often results in lost momentum. But what if your tools remembered everything for you, accurately, securely, and without needing you to copy-paste your brain into every chat window?",[600,604,605,606,609,610,613,614,617],{},"That’s exactly what ",[607,608,8],"strong",{}," is enabling with the integration of ",[607,611,612],{},"Model Context Protocol (MCP)"," and ",[607,615,616],{},"Long-Term Memory (LTM)",".",[619,620,622],"h2",{"id":621},"what-is-mcp-model-context-protocol-exactly","What is MCP (Model Context Protocol) exactly?",[600,624,625],{},"MCP is an open standard developed by Anthropic that enables Large Language Models (LLMs) to communicate with external data sources and tools without needing custom integrations for each one.",[600,627,628,631],{},[607,629,630],{},"Think of it as",": An API for the AI to get context, but it is easier for the AI to use it rather than the API.",[600,633,634,635,642],{},"For a more detailed guide, you can check out ",[636,637,641],"a",{"href":638,"rel":639},"https:\u002F\u002Fpieces.app\u002Fblog\u002Fmcp",[640],"nofollow","this article"," written by one of colleagues.",[619,644,646],{"id":645},"what-is-ltm-long-term-memory","What is LTM (Long-Term Memory)?",[600,648,649,654],{},[636,650,653],{"href":651,"rel":652},"https:\u002F\u002Fpieces.app\u002Ffeatures\u002Flong-term-memory",[640],"Pieces LTM"," is an advanced memory engine that:",[656,657,658,662,665,668],"ul",{},[659,660,661],"li",{},"Captures workflow data automatically (code snippets, browser history, conversations)",[659,663,664],{},"Stores everything locally for privacy and security",[659,666,667],{},"Provides temporal grounding (time-based queries)",[659,669,670,671,674],{},"Tracks application sources ",[607,672,673],{},"securely"," (where memories originated)",[600,676,677,678,683],{},"and if you know by now, long-term memory is basically ",[636,679,682],{"href":680,"rel":681},"https:\u002F\u002Fpieces.app\u002Fblog\u002Flong-term-memory",[640],"everything you need"," for a smarter and more efficient AI workflow.",[619,685,687],{"id":686},"how-they-work-together","How they work together",[600,689,690],{},[691,692],"img",{"alt":693,"src":694},"Diagram showing how the MCP server collects activity from various apps and environments, processes it locally, and sends it to Pieces' long-term memory engine. Arrows represent data flow from sources like code editors, browsers, and terminals through MCP, into memory storage, which is then accessed by the Pieces Copilot for context-aware AI interactions.","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Fmcp-memory\u002Fimg-001.png",[600,696,697,698,617],{},"For a deeper dive, check out the behind-the-scenes details of ",[636,699,702],{"href":700,"rel":701},"https:\u002F\u002Fpieces.app\u002Fblog\u002Finside-mcp-server-implementation",[640],"implementing the MCP server",[619,704,706],{"id":705},"lets-see-how-llm-interact-with-mcp","Let's see how LLM interact with MCP",[600,708,709],{},"Let's use the PiecesCLI to try to get the memories from the LTM.",[711,712,717],"pre",{"className":713,"code":715,"language":716},[714],"language-text","bash\npip install pieces-cli\n# OR\nbrew install pieces-cli\n","text",[718,719,715],"code",{"__ignoreMap":720},"",[711,722,725],{"className":723,"code":724,"language":716},[714],"pieces mcp start\n",[718,726,724],{"__ignoreMap":720},[728,729,730],"ol",{},[659,731,732],{},"Let's initialize the connection",[711,734,737],{"className":735,"code":736,"language":716},[714],"{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"initialize\",\"params\":{\"protocolVersion\":\"2024-11-05\",\"capabilities\":{},\"clientInfo\":{\"name\":\"pieces-cli-user\",\"version\":\"1.0.0\"}}}\n",[718,738,736],{"__ignoreMap":720},[600,740,741],{},"Response:",[711,743,746],{"className":744,"code":745,"language":716},[714],"{\"jsonrpc\":\"2.0\",\"id\":1,\"result\":{\"protocolVersion\":\"2024-11-05\",\"capabilities\":{\"experimental\":{},\"tools\":{\"listChanged\":false}},\"serverInfo\":{\"name\":\"pieces-stdio-mcp\",\"version\":\"0.1.0\"}}}\n",[718,747,745],{"__ignoreMap":720},[728,749,750],{},[659,751,752],{},"Let's send a notification to the MCP server to say that we are ready to use.",[711,754,757],{"className":755,"code":756,"language":716},[714],"{\"jsonrpc\":\"2.0\",\"method\":\"notifications\u002Finitialized\",\"params\":{}}\n",[718,758,756],{"__ignoreMap":720},[600,760,761],{},"Notifications do not have a response; they are like sending a message to the server.",[728,763,764],{},[659,765,766],{},"Let's list the tools that the MCP server has to offer. Response:",[711,768,771],{"className":769,"code":770,"language":716},[714],"{\"jsonrpc\":\"2.0\",\"id\":2,\"method\":\"tools\u002Flist\",\"params\":{}}\n",[718,772,770],{"__ignoreMap":720},[600,774,775],{},"Response is long, but we can see that we have two tools:",[711,777,780],{"className":778,"code":779,"language":716},[714],"{\n    \"jsonrpc\": \"2.0\",\n    \"id\": 2,\n    \"result\": {\n        \"tools\": [\n            {\n                \"name\": \"ask_pieces_ltm\",\n                \"description\": \"Ask Pieces a question to retrieve historical\u002Fcontextual information from the user's environment.\",\n                \"inputSchema\": {\n                    \u002F\u002F Schema defined to the llm model to follow when calling the tool \u002F\u002F\n                }\n            },\n            {\n                \"name\": \"create_pieces_memory\",\n                \"description\": \"Use this tool to capture a detailed, never-forgotten memory in Pieces. Agents and humans alike—such as Cursor, Claude, Perplexity, Goose, and ChatGPT—can leverage these memories to preserve important context or breakthroughs that occur in a project. Think of these as \\\"smart checkpoints\\\" that document your journey and ensure valuable information is always accessible for future reference. Providing thorough file and folder paths helps systems or users verify the locations on the OS and open them directly from the workstream summary.\",\n                \"inputSchema\": {\n                        \u002F\u002F Schema defined here as wee\n                }\n            }\n        ]\n    }\n}\n",[718,781,779],{"__ignoreMap":720},[619,783,785],{"id":784},"implementation-details","Implementation details",[787,788,790],"h3",{"id":789},"available-mcp-tools","Available MCP tools",[600,792,793],{},"The Pieces MCP server exposes two primary tools:",[728,795,796],{},[659,797,798,801],{},[718,799,800],{},"ask_pieces_ltm"," - Memory Retrieval Tool",[600,803,804,807],{},[607,805,806],{},"Purpose",": Retrieve historical\u002Fcontextual information from your development environment.",[600,809,810,813],{},[607,811,812],{},"Parameters",":",[711,815,818],{"className":816,"code":817,"language":716},[714],"{\n  question: string;        \u002F\u002F Required: User's query\n  topics?: string[];       \u002F\u002F Topical keywords for context\n  open_files?: string[];   \u002F\u002F Currently open files in IDE\n  application_sources?: string[];  \u002F\u002F Filter by apps (Chrome, VS Code, Discord, etc.)\n  chat_llm: string;        \u002F\u002F Required: LLM being used (e.g., \"gpt-4o-mini\")\n  related_questions?: string[];    \u002F\u002F Supplementary questions\n  connected_client?: string;       \u002F\u002F Client name (Cursor, Claude, etc.)\n}\n",[718,819,817],{"__ignoreMap":720},[728,821,823],{"start":822},2,[659,824,825,828],{},[718,826,827],{},"create_pieces_memory","-  Memory Creation Tool",[600,830,831,833],{},[607,832,806],{},": Capture detailed memories for future reference by humans and AI.",[600,835,836,813],{},[607,837,812],{},[711,839,842],{"className":840,"code":841,"language":716},[714],"{\n  summary_description: string;  \u002F\u002F Required: 1-2 sentence summary\n  summary: string;             \u002F\u002F Required: Detailed markdown narrative\n  project?: string;            \u002F\u002F Absolute path to project root\n  files?: string[];            \u002F\u002F Absolute file\u002Ffolder paths\n  externalLinks?: string[];    \u002F\u002F URLs to docs, repos, etc.\n  connected_client?: string;   \u002F\u002F Client creating the memory\n}\n",[718,843,841],{"__ignoreMap":720},[787,845,847],{"id":846},"running-a-tool-call","Running a tool call",[711,849,852],{"className":850,"code":851,"language":716},[714],"{\n  \"method\": \"tools\u002Fcall\",\n  \"params\": {\n    \"name\": \"ask_pieces_ltm\",\n    \"arguments\": {\n      \"question\": \"what I was working on yesterday\",\n      \"topics\": [\n        \"cli\",\n        \"pieces-cli\",\n        \"pieces\"\n      ],\n      \"open_files\": [],\n      \"application_sources\": [\n        \"Google Chrome\",\n      ],\n      \"chat_llm\": \"gpt-4o-mini\",\n      \"related_questions\": [],\n      \"connected_client\": \"pieces-cli-user\"\n    },\n  }\n}\n",[718,853,851],{"__ignoreMap":720},[600,855,856],{},"and the Pieces LTM returned a response like that",[711,858,861],{"className":859,"code":860,"language":716},[714],"{\n  \"content\": [\n    {\n      \"type\": \"text\",\n      \"text\": \"\" \u002F\u002F Here went I was working on but in details\n    }\n  ],\n  \"isError\": false\n}\n",[718,862,860],{"__ignoreMap":720},[600,864,865,866],{},"One of the great tools that helps in debugging and checking MCP payloads in the Inspector ",[718,867,868],{},"npx @modelcontextprotocol\u002Finspector",[619,870,872],{"id":871},"how-to-benefit-from-long-term-memory","How to benefit from Long term memory",[600,874,875],{},"Pieces LTM is not just a cache or a clipboard history, it’s a fully-featured, locally stored memory system. It captures workflow data in real-time, from your IDE to your browser to your documentation. Everything is stored locally via PiecesOS, ensuring privacy, security, and full control.",[600,877,878,879],{},"But it goes further by tracking timestamps, application sources, and even grounding context temporally, so you can ask time-relative questions like, ",[718,880,881],{},"“What was I debugging yesterday?”",[600,883,884],{},"You don’t need to organize these memories manually. They're structured, timestamped, and retrievable using semantic queries. This opens up a new kind of productivity, where every step in your workflow is archived and accessible, whether for debugging, knowledge sharing, or just picking up where you left off.",[619,886,888],{"id":887},"let-memory-follow-you","Let memory follow you",[600,890,891,892,897,898,903],{},"One of the most powerful aspects of this architecture is memory continuity. You could start a task in ",[636,893,896],{"href":894,"rel":895},"https:\u002F\u002Fpieces.app\u002Ffeatures\u002Fmcp\u002Fcursor",[640],"Cursor",", switch to Slack to discuss an issue, and later revisit it ",[636,899,902],{"href":900,"rel":901},"https:\u002F\u002Fpieces.app\u002Fplugins\u002Fvs-code",[640],"in VS Code",", all without needing to remember every step or detail.",[600,905,906],{},"Your AI assistant will still have the full picture, because Pieces LTM has been silently capturing your context across time and space.",[600,908,909,910,914,915,918,919,922],{},"Normally, you either have to overshare context in every prompt ",[911,912,913],"em",{},"(“I was debugging the auth module in our CLI yesterday…”",") or risk vague, unhelpful results ",[911,916,917],{},"(“Fix this error pls”)",". But with Pieces, you can simply say: ",[718,920,921],{},"“Hey Cursor, fix the error I hit yesterday,”"," and the assistant has enough background to understand what you mean.",[600,924,925],{},"It’s a new paradigm of context portability, and it’s local, private, and seamless.",[619,927,929],{"id":928},"use-context-smart","Use context smart",[600,931,932],{},"MCP and LTM don’t just make your workflow smoother, they redefine the relationship between developer and AI. They’re not just tools, they’re infrastructure. With memory that never forgets and context that travels with you, the time you used to spend explaining, remembering, or retracing steps is now spent building.",[600,934,935],{},"Context is no longer fragile. It's durable, accessible, and, most importantly, yours.",{"title":720,"searchDepth":822,"depth":822,"links":937},[938,939,940,941,942,947,948,949],{"id":621,"depth":822,"text":622},{"id":645,"depth":822,"text":646},{"id":686,"depth":822,"text":687},{"id":705,"depth":822,"text":706},{"id":784,"depth":822,"text":785,"children":943},[944,946],{"id":789,"depth":945,"text":790},3,{"id":846,"depth":945,"text":847},{"id":871,"depth":822,"text":872},{"id":887,"depth":822,"text":888},{"id":928,"depth":822,"text":929},"Get started","https:\u002F\u002Fpieces.app\u002F","AI & LLM","2025-08-07T08:30:40.390Z","Pieces’ MCP and long-term memory are quietly becoming go-to tools for developers who want smarter, privacy-first AI that just works in the background.",false,"md","https:\u002F\u002Fstorage.googleapis.com\u002Fpieces-marketing-website\u002Fimages\u002Fblog\u002Fmcp-memory\u002Fhero.png",{},"\u002Fblog\u002Fmcp-memory",{"title":591,"description":954},"blog\u002Fmcp-memory","7zThfktXKqLwa4i7A3S82c1PC2l-rToFFsQQJ984zP4",{"id":964,"title":592,"body":965,"description":969,"draft":955,"extension":956,"meta":972,"navigation":20,"path":973,"photo":593,"photoAlt":594,"seo":974,"stem":975,"__hash__":976},"authors\u002Fauthors\u002Fbishoy-hany.md",{"type":597,"value":966,"toc":970},[967],[600,968,969],{},"Bishoy Hany is a Software Engineer at Pieces, where he plays a key role in building developer-first AI experiences that run locally and respect user privacy. With a strong focus on performance, reliability, and intelligent automation, Bishoy works across the full stack to help shape the future of long-term memory and contextual AI.",{"title":720,"searchDepth":822,"depth":822,"links":971},[],{},"\u002Fauthors\u002Fbishoy-hany",{"title":592,"description":969},"authors\u002Fbishoy-hany","_m1q8mqNk1I4j1V0_YJSK0zuMS48DBT4Nqw9e3PZjMM"]