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"MCP server for contextual codebase analysis and task-focused code understanding" matching MCP servers:

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    MCP server for GreyNoise threat intelligence, enabling IP analysis, GNQL queries, tag and vulnerability lookups, and session/pcap retrieval via natural language.
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    MIT
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    Enables gene set enrichment analysis using the Enrichr API across hundreds of gene set libraries including Gene Ontology, pathways, diseases, tissues, drugs, and transcription factors. Returns only statistically significant results for interpretation.
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    MIT
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    Enables AI agents to retrieve real-time data on wallet DeFi positions, token balances, and NFT holdings across multiple blockchains. It supports hundreds of protocols and provides specialized tools for chain-specific or protocol-specific portfolio analysis.
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    MIT
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    Local MCP server for generating image assets with Google Gemini (Nano Banana 2 / Pro). Supports transparent PNG/WebP output, exact resizing/cropping, up to 14 reference images, and Google Search grounding.
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    MIT
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    A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.
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    AGPL 3.0
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    Unofficial MCP server for the messdienst24.de utility portal, providing tools to retrieve heating and hot-water consumption data, download PDF reports, and compare usage trends.
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    3
    MIT
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    The MCP server for Nanostores that scans your codebase with AST analysis to find stores, subscribers, and dependency chains, and can ingest live events to spot noisy, error-prone, or unused stores.
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    An MCP server that turns AI assistants into competent Festo/CODESYS PLC engineers by grounding them in curated, manufacturer-accurate knowledge and forcing every artifact through a machine-checked validation pipeline for CODESYS import.
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    Model Context Protocol (MCP) server for GitLab — exposes 1006 GitLab REST & GraphQL API operations as MCP tools (42 meta-tools / 57 enterprise), 24 resources, 38 prompts, and 17 completion types for AI assistants. Written in Go, single static binary, stdio and HTTP transport.
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    MIT