The Meko MCP Server provides the primary tools for managing datapacks, conversations, memories, knowledge base, and related search.

To connect from IDEs and MCP-compatible clients, use the Meko MCP URL https://mcp.mekodata.ai/mcp and a user API key from Settings>API Keys. See Integrations and Quick start.

Free tier quotas

On the Free tier, MCP tool calls are metered per account:

Metric	Limit (per month)	Example tools
Conversations	1,000	conversation_add_message
Retrievals	10,000	context_search, memory_search, knowledgebase_search

When a quota is exceeded, further calls are blocked until you upgrade. Datapack creation and knowledge-base uploads are capped separately. See Meko pricing for the full plan table.

Tools

This page documents 28 Meko MCP tools. Experimental conversation-search tools are hidden unless a deployment explicitly enables them.

Authorization comes from the authenticated user and their datapack grants.

Use	For
Context search context_search	Open-ended recall across personal memory, shared knowledge, and past conversations. Example: What do you know about this project?
Conversation conversation_*	Full saved chat history and thread replay. Example: Save or retrieve this chat
Memory memory_*	Durable facts, preferences, decisions, and useful outcomes you want to recall across agents. Example: Remember this about me or the project
Knowledge base knowledgebase_*	Reusable, persistent, indexed shared documents, and collective memories that can be searched across agents. Example: Search this in the knowledge base
Datapack datapack_*	Provisioning and managing Meko workspaces/environments. Example: Create or manage a workspace
Artifact artifact_*	Uploading and retrieving files (reports, CSVs, PDFs, code output) for later or cross-agent access. Example: Save this file for later

Context search

Tool

Description

context_search

Search personal memory, the datapack knowledge base, and the past-conversation cache in parallel. Use this for open-ended recall when the answer could be in more than one source. Results stay in separate memory, knowledge_base, and conversation buckets so one source doesn't hide another. The conversation bucket can include conversations from every user and agent on the datapack. Check each bucket's source status before treating an empty result as proof that nothing is stored. Call memory_search or knowledgebase_search directly when you need one source or need to confirm a failed source. force_source can limit the call to conversation, memory, or knowledge_base; the default auto searches all three. Daily activity, token savings, and free-tier usage are recorded in the background, so that write does not delay the result. Typical inputs: query, conversation_id, agent_id, datapack_id, limit, force_source

Conversation tools

Tool	Description
conversation_create	Create a stored conversation container. Use this to save a chat/thread, not just a fact or document. Typical inputs: agent_id, session_id, datapack_id, title, run_id, metadata
conversation_add_message	Add a user/assistant turn to a stored conversation. The optional plan is an array of action strings. Use this to preserve full exchanges; Meko asynchronously extracts durable facts from the saved user turn. Typical inputs: conversation_id, input, output, agent_id, reasoning, plan, metadata, seed, datapack_id
conversation_get	Fetch an agent-owned conversation, optionally with messages. The supplied agent_id must own the conversation. Use this to review or replay a past conversation. Typical inputs: conversation_id, agent_id, include_messages, limit, offset, datapack_id
conversation_list	List conversations for a datapack across the current user's agent namespaces. agent_id is used for trace attribution, not result filtering. Typical inputs: datapack_id, conversation_id, agent_id, limit, offset
conversation_update	Update an agent-owned conversation's title or metadata. Use this when organizing or relabeling stored conversations. Typical inputs: conversation_id, agent_id, title, metadata, datapack_id
conversation_delete	Permanently delete an agent-owned conversation and all its messages. Use this to remove saved conversation history. Typical inputs: conversation_id, agent_id, datapack_id
track_token_usage	Record a token-usage observation against a conversation trace, so that LLM, embedding, or extraction spend is attributed for free-tier accounting and dashboards. Use this to pair a billable model call with a recorded usage entry. Typical inputs: conversation_id, name, input_tokens, output_tokens, total_tokens, model, message_id, datapack_id

Memory tools

Tool	Description
memory_add	Store durable facts, preferences, decisions, or useful outcomes. Use this for direct "remember this" requests, facts found only in assistant/tool output, and corrections. Saved conversation turns are extracted automatically. Typical inputs: text, conversation_id, agent_id, run_id, metadata, messages, datapack_id
memory_search	Search the current user's memories across agent namespaces with semantic, keyword, and entity-aware ranking. Results include memories you promoted, along with your private memories. Results are ordered by score. raw_similarity_score is the vector store's cosine similarity and does not determine the order by itself. agent_id is used for trace attribution, not result filtering; don't fan out one search per agent. limit sets the maximum number of returned results (default: 10). Keep the result set small enough for the model context. A response with error or an error-shaped detail means the search failed. Only a successful response with an empty results array means no matching memories were found. Typical inputs: query, conversation_id, agent_id, limit, datapack_id, run_id
memory_get_all	Return a recent window of roughly 20 memories plus the total count across the current user's agent namespaces. This is not a complete list or export and doesn't accept pagination arguments. Use memory_search for older memories or memory_get_by_id when you know the ID. agent_id is used for trace attribution, not result filtering. Typical inputs: conversation_id, agent_id, datapack_id, promoted
memory_get_by_id	Retrieve a single memory by ID. Use this to inspect a memory. Typical inputs: memory_id, conversation_id, agent_id, datapack_id
memory_update	Replace the text of an existing memory. Use this to correct a remembered fact; agent_id must match the writer. Typical inputs: memory_id, text, conversation_id, agent_id, datapack_id
memory_delete_by_id	Delete one memory. Use this to remove a specific stored memory; agent_id must match the writer. Typical inputs: memory_id, conversation_id, agent_id, datapack_id
memory_delete_all	Delete memories for an agent, optionally narrowed by run ID. Use this for a full reset or broad cleanup. Typical inputs: conversation_id, agent_id, run_id, datapack_id
memory_promote	Move memories into the datapack's shared knowledge layer, removing them from the user's private store. Before this one-way action, preview the exact memories, explain team visibility and private-memory removal, and get explicit confirmation. Only datapack owners and maintainers may promote. See Learnings. Typical inputs: conversation_id, memory_ids, agent_id, datapack_id

Knowledgebase tools

Tool	Description
knowledgebase_search	Perform hybrid semantic and keyword search over a datapack's knowledge base. Use this to search uploaded and shared documents. Typical inputs: query, conversation_id, datapack_id, agent_id, limit
knowledgebase_delete_document	Permanently delete one uploaded file and its chunks. Use this only after the user confirms. The datapack owner, a maintainer, or the file's uploader can delete it. Take document_id from a knowledgebase_search hit. Hits don't include the file name. You can't delete a file until it finishes indexing. Typical inputs: document_id, conversation_id, datapack_id
knowledgebase_upload_document	Request a presigned URL to upload one file to a datapack's knowledge base. Use this when you want the content to be searchable later. To keep a file exactly as-is and get it back whole, use artifact_put instead. Accepts PDF, TXT, TEXT, MD, JSON, CSV, XML, HTML, and HTM; text files must be UTF-8. The tool doesn't send the file. The agent POSTs it to the returned URL, so it needs the file's bytes and outbound HTTPS access. Send the file's first 3,072 bytes as content_head_base64. Meko checks that sample against the extension before it issues the URL. After the file arrives, Meko checks the stored bytes and indexes them. The agent does not have to confirm the upload. The URL only accepts a file of exactly size bytes, with SHA-256 content_hash, for 5 minutes. If identical content is already in the datapack, the response is already_uploaded: true and there's nothing to upload. The per-file size cap depends on your plan: 5 MB on Free, 50 MB on Standard, and 60 MB on Pro. A larger size is refused with VALIDATION_ERROR and no URL. An owner or a maintainer can upload. Typical inputs: filename, size, content_hash, content_head_base64, conversation_id, datapack_id
knowledgebase_document_status	Report the upload and indexing state of a file uploaded with knowledgebase_upload_document. Poll with the upload response's key as document_key. state is awaiting_upload, indexing, completed, failed, abandoned, or oversized. abandoned means the upload expired, or the stored file was not the type or size that was declared. Read message before you request another URL. oversized means the file is over the cap it was authorized for. Typical inputs: document_key, conversation_id, datapack_id

Datapack tools

Tool	Description
datapack_create	Provision a new datapack. Use this to set up a new isolated Meko environment. Typical inputs: name, conversation_id, datapack_id
datapack_describe	Return datapack details and optional status. Use this to inspect an existing datapack. Typical inputs: datapack_id, include_status, conversation_id
datapack_search	Find datapacks visible to the current user, one page at a time. Use this to find a datapack's ID. It works for accounts of any size. query matches part of the name (case-insensitive), and name matches the exact name. grant limits results to datapacks you own (owner) or that are shared with you (shared). sort is created (newest first, the default) or name. The response is {datapacks, has_more, next_cursor, total}. While has_more is true, get the next page by repeating the call with the same filters and passing next_cursor as cursor. A Meko API that predates paging returns has_more: true with an empty next_cursor when more rows match; narrow the search with query then. limit sets the page size, 1-100 (default: 20). total is null unless you pass include_total. Typical inputs: query, name, limit, cursor, grant, sort, include_total, conversation_id
datapack_list	List datapacks visible to the current user as one array. Use this for an account with a small number of datapacks. To find one datapack, use datapack_search. With no query or limit, it returns every datapack, up to 1,000 by default. Above that, it returns a too_many_datapacks error. With query and no limit, it returns every datapack whose name contains query, and returns too_many_datapacks if more than 100 match. With limit, it returns the first limit matches (at most 100) and doesn't say whether more exist. Typical inputs: conversation_id, query, limit, datapack_id
datapack_update	Update a datapack's name, description, and/or conversation-search opt-out. At least one of name, description, or conversation_search_opt_out must be provided; clearing a description with an empty string isn't supported. Conversation search is on by default — opting out (restricted to the datapack's owner or a maintainer) is also available from the portal. Typical inputs: datapack_id, name, description, conversation_search_opt_out, conversation_id
datapack_delete	Permanently delete a datapack. This operation is irreversible and requires an explicit datapack_id. Typical inputs: datapack_id, conversation_id

Artifact tools

Tool	Description
artifact_put	Upload a file artifact (report, CSV, PDF, code output, and so on) to a datapack for later retrieval or cross-agent sharing. Use this when an agent has generated a file it wants to persist. To make a file's content searchable instead, use knowledgebase_upload_document. Uploading the same bytes twice is idempotent and returns the same content hash, which is the lookup key for artifact_get. Typical inputs: filename, content_base64, content_type, conversation_id, datapack_id, agent_id
artifact_get	Retrieve a previously uploaded artifact by its content hash. Use this when an agent needs to read back a file it, or another agent, previously stored using artifact_put. Files smaller than 1 MB are returned inline; larger files are written to the client's local artifact directory and returned by path. Typical inputs: content_hash, conversation_id, datapack_id, agent_id

Files under 1 MB are stored inline in the database; files 1 MB and larger are stored in the datapack's object storage and written to a local path on retrieval. Artifacts are content-addressed by SHA-256, so uploads are idempotent. On the Free tier, artifacts are soft-expired 30 days after last access; Standard and Pro have no expiry. Each artifact can be up to 5 MB on every plan; this is separate from the knowledge-base per-file caps above. Expired artifacts are recoverable by re-upload. See Work with artifacts.