Tag

#Context

997 results found

Context Repo Mcp Server
@ContextRepo

*One workspace for your prompts, documents, and collections — accessible from every AI client you use.* Context Repo is an AI context management platform for capturing, organizing, versioning, and searching the knowledge artifacts you use with AI tools. This MCP server exposes **28 tools** that give Claude, Cursor, ChatGPT, Factory, Windsurf, Codex, Claude Code, and any other MCP-compatible client direct read-and-write access to your personal workspace — no copy-paste, no context loss between conversations. ## Features - **Prompts** — Full CRUD with version history, rollback, and semantic search across your prompt library - **Documents** — Markdown and plain-text storage with automatic chunking and 1536-dim vector embeddings - **Collections** — Named folders that group prompts and documents into project-scoped contexts - **Catalog Search** — `find_items` returns ranked results across prompts, documents, and collections in a single call (semantic by default, literal fallback) - **Deep Search** — Hierarchical document navigation: search ranked passages, then expand up to parents, down to children, or sideways to siblings for token-efficient exploration of long content - **Version History** — Every content edit creates a new version; restore any prompt or document to any previous state - **Real-time Sync** — Changes propagate instantly via Convex live queries; AI clients always see the latest content - **ChatGPT Apps Ready** — `search` and `fetch` are OpenAI Apps SDK Company-Knowledge eligible; `find_items` carries an optional `ui://` resource for inline result rendering ## Tools (28 total) | Category | Tools | |---|---| | User Info (1) | `get_user_info` | | Prompts (7) | `search_prompts` · `read_prompt` · `create_prompt` · `update_prompt` · `delete_prompt` · `get_prompt_versions` · `restore_prompt_version` | | Documents (7) | `list_documents` · `get_document` · `create_document` · `update_document` · `delete_document` · `get_document_versions` · `restore_document_version` | | Collections (7) | `list_collections` · `get_collection` · `create_collection` · `update_collection` · `delete_collection` · `add_to_collection` · `remove_from_collection` | | Catalog (1) | `find_items` | | Deep Search (3) | `deep_search` · `deep_read` · `deep_expand` | | ChatGPT Apps (2) | `search` · `fetch` | ## Authentication Two ways to connect: 1. **API Key** — Generate a scoped `gm_*` key at [contextrepo.com/dashboard/settings](https://contextrepo.com/dashboard/settings). Per-key permissions: `prompts.read`, `prompts.write`, `documents.read`, `documents.write`, `documents.scrape`. Sent as `Authorization: Bearer gm_...`. 2. **Clerk OAuth 2.0** — RFC 9728 protected-resource metadata at `/.well-known/oauth-protected-resource/mcp`, RFC 8414 authorization-server metadata at `/.well-known/oauth-authorization-server`. Standard OAuth flow for clients that support it. ## Use Cases - **Prompt library that follows you.** Stop copy-pasting prompts between Claude, Cursor, and ChatGPT. Maintain one canonical version, retrieve it from any client. - **Personal knowledge base.** Save research articles, documentation, and AI conversations with the [Context Repo Chrome Extension](https://contextrepo.com/chrome-extension), then pull them in as grounded context inside your AI tools. - **Project-scoped collections.** Separate workspaces per client, repo, or topic so an AI assistant only sees what's relevant to the task at hand. - **Version-controlled prompts.** Track how prompts evolve, A/B test variants, and roll back when an "improvement" turns out worse. - **Long-document exploration.** Deep Search navigates book-length documents passage by passage instead of dumping them into context — every chunk carries parent/child/sibling links the agent can walk. ## Compatibility Streamable HTTP transport, MCP spec ≥ 2025-03-26. Verified with Claude Desktop, Cursor, ChatGPT (via the OpenAI Apps SDK), Factory, Windsurf, Codex, Claude Code, VS Code (Continue), and Amp. Any MCP-compatible client should work. ## Resources - Website: [contextrepo.com](https://contextrepo.com) - Documentation: [contextrepo.com/docs](https://contextrepo.com/docs) - Pricing & free trial: [contextrepo.com/pricing](https://contextrepo.com/pricing) - Agent discovery: [contextrepo.com/llms.txt](https://contextrepo.com/llms.txt) - API reference: [contextrepo.com/openapi.json](https://contextrepo.com/openapi.json)

a month ago
Memtrace
@syncable-dev

Memtrace — Structural Memory for AI Coding Agents The Problem Every AI coding agent — Claude Code, Cursor, Codex, Copilot — starts each turn completely blank. It re-reads raw source files and re-derives the full call graph, type hierarchy, and import tree from scratch on every single invocation. That structural rework burns 60–90% of the context window before any real reasoning begins. Less than 5% of tokens in a typical agentic coding session contribute genuine new intelligence. The rest is expensive, redundant noise — and it compounds: accuracy drops 40% as sessions grow, stale context crowds out signal, and summaries strip out the structural relationships agents need most. The Solution Memtrace is a bi-temporal structural memory layer that turns your codebase into a live, queryable knowledge graph — compiled from the AST, not guessed from embeddings. Every function, class, interface, and API endpoint becomes a typed node with deterministic relationships. Every file save becomes a queryable episode with timestamps, so agents can reason about structure, detect regressions, and time-travel through their own work without re-reading anything. One Rust binary. Zero configuration. Five-minute install. What agents can do with it Find callers, callees, and dependencies instantly — no file scanning, no token waste Compute blast radius before making a change — know exactly what breaks before anything is touched Detect structural drift between sessions — catch regressions the moment they happen, not at PR review Time-travel through code evolution — query any prior state of any symbol, not just git commits Search across the full codebase with hybrid retrieval — BM25 full-text + HNSW vector + graph traversal fused in one query Map API topology across services — cross-repo HTTP call graphs, dependency chains, dead endpoint detection Benefits −90% token cost on structural queries (Mem0) +26% accuracy on multi-step agentic tasks (Mem0) −91% p95 latency on structural lookups vs. RAG baselines +32.8% SWE-bench bug-fix success rate when agents have graph context (RepoGraph) 200–800ms per-save re-indexing — every file save is a queryable episode in under a second 40+ MCP tools covering indexing, search, relationships, impact analysis, temporal evolution, API topology, graph algorithms, and direct Cypher queries 12 languages + 3 IaC formats supported via Tree-sitter grammars Local-first, closed-source Rust — code never leaves the machine, no account required, no telemetry

a month ago
Lexicon
@Nadine

2 months ago