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licenseC
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maintenanceProvides RAG (Retrieval-Augmented Generation) capabilities via Contextual AI, enabling query processing and context-aware responses with citations. Integrates with MCP clients like Cursor IDE and Claude Desktop.
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DecisionPen MCP Server
Officialby DecisionPen
Related Servers
Alternatives to DecisionPen MCP Server
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Related Servers
- AlicenseNot gradedqualityBmaintenanceExposes a shared graph-based context engine as MCP tools for Claude, Copilot, and other AI agents, enabling knowledge ingestion, recall, search, and LLM-ready context assembly across sessions.1Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables Claude to query client and document data in plain English with cited answers, enforce per-client access, safely file incoming documents, and record audited actions through MCP tools.MIT
- AlicenseNot gradedqualityDmaintenanceConnects AI assistants to a team's product knowledge base, enabling search, capture, and navigation of glossary terms, business rules, decisions, and relations via MCP.20,954 npmMIT
Cactus MCP Serverofficial
AlicenseNot gradedqualityCmaintenanceConnects MCP clients such as Claude, ChatGPT, Cursor and VS Code to a product's published help pages, product map, release timeline and past support conversations, letting users search, read, ask questions with cited answers, and run code-backed investigations. It also offers a sign-in-free server per public help center for visitor-style help search and team contact.MIT- AlicenseNot gradedqualityCmaintenanceConnects Claude Desktop to self-hosted enterprise tools such as Confluence, Jira, Bitbucket, Airtable, and databases (MySQL, MariaDB, ClickHouse, Oracle) via MCP.MIT
TDQS
A3.7/5.0
Scored across 18 tools
Disambiguation4/5
Tools split into clearly described workflows (S3 source browsing/ingestion vs. Confirmed-map context vs. Ask fallback), and most boundaries are explicit in the descriptions. A few near-overlaps exist, notably search_docs vs. list_sources, search_evidence vs. get_entity_evidence, and get_relationship_summary vs. get_entity_relationships, but the descriptions provide enough guidance to choose correctly.
Naming Consistency5/5
All tools use snake_case with a predictable verb_noun or verb_resource construction (get_, list_, search_, propose_, run_, rerun_, describe_, ask_). The batch_get_entities prefix is a minor variation but remains clear and consistent with the rest.
Tool Count4/5
Eighteen tools is slightly above the typical sweet spot, but the surface covers two coherent sub-domains (S3 ingestion and DecisionPen context retrieval), and each tool has a distinct role. A few tools (e.g., list_ask_skills, describe_context_model) feel peripheral but still earn their place.
Completeness4/5
The surface covers source discovery/fetch/propose/sync with job polling, full entity search/hydration/relationship/evidence flows, and an Ask fallback. Minor gaps remain around enrichment job listing/history and explicit approval workflows, but these appear intentionally human-gated rather than missing agent operations.
Maintenance
ActivityMaintained
ResponsivenessNo recent community issues