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Search, read, and analyze wiki content with graph and vector tools.
The available material is very limited. Based on known facts, it is open-source, requires no secrets, and declares no remote endpoint, but it can execute code and relies on a persistent knowledge-graph backend, so its actual data boundaries and egress behavior remain insufficiently documented and warrant caution.
The material explicitly states that no keys or environment variables are required, and there is no indication of API tokens, account credentials, or other sensitive authentication material being requested; known credential-abuse exposure appears low.
Although no remote endpoint is declared, the feature description mentions a 'persistent knowledge graph backend,' and the lack of a README prevents confirming whether it runs fully locally or may send queries/page content to a backend; egress boundaries are unclear and require caution.
The system checks explicitly mark this tool as having executes-code capability, indicating it can execute code or start related local processes; this is common for MCP tools, but the current documentation does not describe execution scope or constraints, so it should be run in a controlled environment.
The description says it can read, search, and analyze wiki pages and interact with a persistent knowledge-graph backend; this implies access to locally stored or backend-stored data, but the material does not specify readable/writable paths, permission scope, or isolation mechanisms, making data-access boundaries insufficiently transparent.
A positive factor is that a source repository exists and can be audited. However, it comes from a third-party registry, has no declared license, shows 0 stars, has unknown maintenance status, and lacks a README, so supply-chain trust and auditability remain weak and warrant manual review of repository contents and dependencies.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "OpenCode LLM Wiki MCP Server" yet — see the docs or source repo.
Use vector search to find the wiki pages most relevant to "typical use cases of the Model Context Protocol (MCP)". List the top 5 by relevance and include a one-line summary for each.
A ranked list of the most relevant wiki pages with brief summaries.
Analyze how the wiki page "Knowledge Graph" is connected to other pages. Identify the most important neighboring topics and explain their paths or relationship types.
Key related pages, relationship explanations, and graph-based connection analysis.
Search the wiki for content about "applications of vector search in enterprise knowledge bases" and produce a structured summary of key ideas, related pages, and potential research threads.
A structured topic summary with core ideas, reference pages, and follow-up research directions.
Give AI agents semantic memory and web search for stronger retrieval and reasoning.
Manage Wiki.js pages, search content, and inspect system info via GraphQL.
Connect AI to any MediaWiki wiki for search, reading, and content interaction.
Aggregate encyclopedic, research, and technical docs into one AI-ready knowledge interface.
Search, validate, and cross-reference structured Markdown knowledge vaults for AI workflows.
Adds nodes, edges, and semantic retrieval to agent knowledge graphs.