content-knowledge-mcp
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- AlicenseNot gradedqualityBmaintenanceProvides read-only knowledge search and guarded action proposal tools. Write-like actions require human approval and return traceable evidence with policy events.1MIT
- AlicenseBqualityAmaintenanceEnables AI agents to access and manage project guidelines, documentation, and context through a structured content system with template support and workflow management.3758 PyPIMIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query organizational architecture and governance constraints, returning evidence-grounded answers from documented structures.MIT
- AlicenseAqualityCmaintenanceEnables agents to search and reference trusted, out-of-band ingested source state, run claim-bound document reviews, validate evidence-status proposals against admissibility guards, and build evidence matrices. It keeps proposal, evidence, admissibility, and adjudication separate so agents can propose epistemic statuses without creating the source facts, page locators, provenance, or missingness records that support them.5MIT
- AlicenseAqualityAmaintenanceEnables AI coding agents to query a team's institutional coding rules and lessons learned, resolving a project → framework → language → global scope hierarchy so only the entries relevant to the current task are returned instead of whole knowledge-base files. It also supports governed proposal approval, PR and feature audits, meeting-transcript extraction, optional semantic ranking, and access-controlled audit logging, while keeping Markdown files as the source of truth.2864 PyPIApache 2.0

gnt MCP Serverofficial
AlicenseNot gradedqualityAmaintenanceEnables AI agents to query live, human-approved rules before taking actions, ensuring compliance and reducing errors.30Apache 2.0
TDQS
Scored across 3 tools
Each tool targets a distinct knowledge resource: editorial rules, visual templates, and past post examples. The descriptions clearly distinguish search, list, and get operations despite all supporting query. An agent can reliably choose the correct tool for each content creation step.
All tool names follow the consistent snake_case verb_noun pattern: search_guidelines, list_visual_templates, get_post_examples. The verbs also accurately reflect the operation type, making the naming predictable and scannable.
With only three tools, the server is tightly focused on content knowledge retrieval. Each tool covers a core knowledge domain for content creation—rules, templates, and examples—without redundant or unnecessary additions.
The set covers the main content-making inputs: brand/editorial guidelines, visual template catalog, and performance-calibrated post examples. Minor gaps exist such as fetching a specific rule by ID or retrieving more than three examples, but the tool set supports its purpose effectively.