MCP servers

Enables MCP clients to annotate and classify single human genetic variants in any notation, returning an auditable points-based ACMG verdict with its criteria, gnomAD frequencies, in-silico predictors, ClinVar data, and per-transcript consequences. It is read-only, requires no account or API key, and keeps answers compact while linking back to the source.

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Bridge any AI coding assistant directly to GenePattern — run bioinformatics modules, manage cloud jobs, and stream results, all through natural language.

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MCP server for B2B lead generation and company search using the Generect Live API. Provides tools to search leads, companies, generate emails, and get lead by URL.

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Enables Claude Desktop to access and manage Geniads' skill library by calling the platform's REST API directly from the local machine, bypassing the hosted MCP connector.

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Enables stdio-only MCP hosts like Claude Desktop and Cursor to securely connect to Payman's remote Genie MCP server, exposing the ask_genie tool for payments and finance tasks via a local bridge.

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Stdio bridge for editors to connect to the GenieOS MCP server, enabling AI agents to interact with GenieOS via Streamable HTTP transport.

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A Model Context Protocol server that provides Claude CLI with a Graphiti knowledge-graph memory backed by Neo4j, featuring synchronous writes and no silent ingestion failures.

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Enables AI agents to use an approved session to inspect capabilities, manage projects and memories, perform bounded reads, and make journaled changes through a stdio interface.

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Provides integration between Genkit and the Model Context Protocol (MCP).

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Retrieve genomic data from EGA, ENA, ENCODE, GEO and NCBI, read bounded regions from indexed BAM/CRAM, VCF and bigWig files, and query source-attributed reference evidence. Runs locally with 23 MCP tools. MIT-licensed; research use.

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GenPark MCP server for normalizing and translating structured data schemas.

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Enables strict resource governance for AI agent processes using Linux cgroup v2, enforcing CPU bandwidth, memory limits, and process limits to prevent resource overconsumption, runaway loops, and fork bombs.

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GenPark MCP server for selecting relevant context under token and latency constraints.

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Enables MCP-compatible agents to execute bash and subprocess commands through a zero-dependency sandbox guard that blocks destructive system mutations, path traversals, reverse shells, and other unsafe execution patterns.

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Enables AI agents to persist and restore checkpoint state, supporting crash recovery and replay for LangGraph-based workflows.

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Enables AI agents and MCP-compliant clients to dynamically shrink long-horizon context windows, pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by roughly 40-70% without frontier LLM round-trips. Runs deterministically as a zero-dependency Python stdio server, accepting a payload plus optional compression and risk-bound options.

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genpark-agent-context-dynamic-compressor-skill

Enables MCP-compatible clients and autonomous agents to dynamically compress long-horizon context by pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by 40-70%. It reduces token burn and latency through deterministic, zero-dependency processing.

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Enables AI agents and MCP clients to dynamically select few-shot in-context examples using semantic token overlap, cosine similarity, and MMR diversity ranking to optimize prompts deterministically without external dependencies.

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genpark-agent-dynamic-few-shot-example-selector-skill

Enables MCP-compliant clients and autonomous agents to pick optimal few-shot in-context exemplars by combining semantic token overlap, cosine similarity, and Maximal Marginal Relevance diversity ranking for prompt optimization. Runs deterministically on the Python standard library with zero external dependencies, avoiding frontier LLM round-trips for sub-millisecond selection.

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genpark-agent-execution-tree-rollback-checkpointer-skill

Enables autonomous agents to snapshot execution-tree and context states, evaluate branch viability, and roll back to previous checkpoints when an exploration path fails without restarting from scratch.

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Enables autonomous agents and MCP clients to decompose text into atomic claims and verify them against reference knowledge, detecting ungrounded claims, numerical contradictions, and hallucinated entities.

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Enables agents to maintain multi-tier memory across working scratchpad, dialogue buffer, and episodic stores with recency decay. Supports hybrid BM25/vector retrieval, rank fusion, graph expansion, and context reordering for optimized MCP-based memory and RAG workflows.

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Enables autonomous agents and MCP-compatible clients to enforce policy-approved actions using risk tiers, reversible checkpoints, and token approval bounds.

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genpark-agent-hybrid-memory-retriever-skill

Enables MCP-compatible agents and IDEs to retrieve episodic long-term memories using hybrid BM25 keyword search, semantic n-gram cosine similarity, and recency decay ranking, with deterministic zero-dependency execution.

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