context-repo-mcp

name	context-repo-mcp
description	Search, retrieve, version, and persist AI prompt templates, documents, and collections in the user's Context Repo account (https://contextrepo.com) via the official MCP server. Use this skill whenever the user mentions their saved prompts, prompt library, prompt templates, system prompts, "context repo", contextrepo, or wants to find, save, version, restore, or share a reusable prompt or document across their AI tools — even when they don't explicitly say "skill" or "MCP". Trigger on phrases like "find my prompt for ...", "use the system prompt I saved as ...", "save this as a reusable prompt", "what prompts do I have about ...", "version this prompt", "restore the previous version of ...", "search my context repo for ...", or "sync this prompt to my Cursor / Claude / ChatGPT setup". Also trigger when the user wants to navigate large documents hierarchically (deep_search -> deep_read -> deep_expand) or perform semantic search across their stored knowledge base.
version	2.2.2
license	MIT
homepage	https://contextrepo.com
repository	https://github.com/Gitmaxd/context-repo-mcp
package	context-repo-mcp
runtime	node
engines	{"node":">=18.0.0"}
auth	["api-key","oauth2"]
tags	["mcp","prompts","prompt-management","knowledge-base","rag","claude","cursor","factory-droid","chatgpt-apps"]

context-repo-mcp

Connect to a Context Repo account and act on the user's saved prompts, documents, and collections through 27 MCP tools. The Context Repo platform stores reusable prompt templates with version history, markdown documents with hierarchical search, and collection-based organization — this skill is the agent's read/write interface to that storage.

Capabilities

Use these tool families for the corresponding intents:

• Prompt retrieval — search_prompts, read_prompt, find_items (semantic + literal cross-content search).
• Prompt creation and versioning — create_prompt, update_prompt, get_prompt_versions, restore_prompt_version, delete_prompt.
• Document storage — list_documents, get_document, create_document, update_document, delete_document, get_document_versions, restore_document_version.
• Collection organization — list_collections, get_collection, create_collection, update_collection, delete_collection, add_to_collection, remove_from_collection.
• Hierarchical document navigation — deep_search -> deep_read -> deep_expand (vector-backed chunk retrieval with parent / sibling navigation across up, down, next, previous, surrounding directions).
• Reasoning — reason (synthesized, cited answer composed across document content, with explicit gaps and conflicts; read-only).
• Account info — get_user_info.

Tool inventory (27 tools, snake_case)

Category	Tools
User	get_user_info
Prompts	search_prompts, read_prompt, create_prompt, update_prompt, delete_prompt, get_prompt_versions, restore_prompt_version
Documents	list_documents, get_document, create_document, update_document, delete_document, get_document_versions, restore_document_version
Collections	list_collections, get_collection, create_collection, update_collection, delete_collection, add_to_collection, remove_from_collection
Cross-search	find_items, deep_search, deep_read, deep_expand
Reasoning	reason

The hosted web MCP at https://contextrepo.com/mcp additionally exposes the OpenAI Apps SDK aliases search and fetch (29 tools there); this npm / stdio package ships the 27 tools above.

Read-only tools are annotated with readOnlyHint: true so safe-mode agents can execute them without write confirmation.

Output format

Every tool returns an MCP CallToolResult of shape { content: [{ type: "text", text: <string> }] }. The text payload format depends on the tool family — agents must dispatch parsing accordingly:

• Read / list / create / update tools (most tools, e.g. read_prompt, search_prompts, list_documents, get_collection, update_prompt, add_to_collection) return stringified JSON (pretty-printed, 2-space indent). Parse with JSON.parse(result.content[0].text) to consume.
• Delete tools (delete_prompt, delete_document, delete_collection) return a plain success or no-op string: "✓ Deleted prompt <id>" or "<X> was already deleted (no-op).". Do not attempt to JSON-parse these.
• Search and navigation tools (find_items, deep_search, deep_read, deep_expand, get_prompt_versions, get_document_versions) return human-readable markdown intended for direct surface to the user. IDs needed for follow-up calls are embedded as bullet fields like **chunkId:** <id> or **id:** <id> — extract via regex when chaining calls.
• Errors return "Error: <message>" in the text block.

Installation

npx context-repo-mcp

Or via Claude Desktop's claude_desktop_config.json:

{
  "mcpServers": {
    "context-repo": {
      "command": "npx",
      "args": ["-y", "context-repo-mcp"],
      "env": { "CONTEXT_REPO_API_KEY": "gm_..." }
    }
  }
}

Generate an API key at https://contextrepo.com/dashboard/settings.

Authentication

Two equivalent auth modes:

1. API Key (recommended for local agents): send Authorization: API-Key gm_.... Generate at https://contextrepo.com/dashboard/settings.
2. OAuth 2.0 (recommended for hosted MCP clients): Clerk-issued bearer token. Discover via https://contextrepo.com/.well-known/oauth-protected-resource/mcp (RFC 9728).

The MCP server's tools/list, initialize, and */list capability methods are publicly callable, so agents can introspect this skill before completing OAuth.

Rate limits

Per authenticated user, sliding window:

• scrape family: 10 requests / 60s
• api family: 100 requests / 60s
• readonly family: 120 requests / 60s

Honor Retry-After headers on 429 responses.

Examples

Find and use a saved prompt

User input: "find my code review prompt and use it for this PR"

1. Call find_items(query="code review")
2. Parse JSON from content[0].text → extract prompt id from results
3. Call read_prompt(promptId=<id>)
4. Parse JSON from content[0].text → use returned `content` field as
   the system message for the PR review.

Save a new reusable prompt

User input: "save this as my standard bug triage prompt"

1. Call create_prompt(
     title="Bug triage",
     description="Standard bug triage workflow",
     content=<the prompt body>,
     engine="claude-3"
   )
2. Parse JSON from content[0].text → confirm new prompt id to user.

Navigate a large document hierarchically

User input: "summarize chapter 5 of my onboarding doc"

1. Call deep_search(query="chapter 5 onboarding")
   → returns markdown listing chunks; extract chunkId via
     `**chunkId:** <id>` line.
2. Call deep_read(chunkId=<id>)
   → returns markdown chunk detail with hierarchy.
3. Call deep_expand(chunkId=<id>, direction="down")
   → returns child chunks for the full chapter.
4. Synthesize summary from concatenated chunk content.

Documentation

• Full MCP guide: https://contextrepo.com/docs/mcp-server
• Tool reference: https://contextrepo.com/docs/mcp-server/tools-reference
• Integration guides:
  • Claude Desktop: https://contextrepo.com/docs/integrations/claude-desktop
  • Cursor: https://contextrepo.com/docs/integrations/cursor
  • ChatGPT Apps: https://contextrepo.com/chatgpt-app

Support

• Issues: https://github.com/Gitmaxd/context-repo-mcp/issues
• Email: [e-mail]
• Status: https://contextrepo.com/status