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Efficient context management: code search, evidence packs, and memory for coding agents.
Efficient context management: code search, evidence packs, and memory for coding agents.
Valid MCP server (2 strong, 5 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry. Trust signals: 3 highly-trusted packages.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-inferensys-contextful": {
"args": [
"-y",
"@inferensys/contextful"
],
"command": "npx"
}
}
}From the project's GitHub README.
Most-efficient Context Management Layer for Agentic AI.
Contextful is a runtime contextual layer agents need for real projects. Available as an MCP, it integrates with Codex, Claude Code, Cursor, Windsurf, GitHub Copilot, VS Code, Cline, Roo Code, Continue, and Zed, then gives agents one fast way to find, compress, cite, and remember project context.
Instead of making an agent read 40 files every session, Contextful indexes the project once and returns a ranked, cited, token-budgeted context pack.
Contextful analyzes the query, classifies intent, and combines lexical search, symbols, docs, graph relationships, and memory hits to retrieve the right evidence. The goal is Google-level project search for agents: vague queries like "resources for auth onboarding" should still land on the right code, docs, and prior lessons.
The default local store is SQLite with FTS-backed search and typed graph tables. V1 ships with:
nodes, edges, node_props, edge_props.The next storage upgrades are optional semantic vectors through sqlite-vec, LanceDB, or local HNSW, and compressed adjacency lists with Roaring bitmaps or CSR arrays for larger repositories.
Agents can store lessons, decisions, and useful project facts, but not as loose "remember this" notes. Every memory requires evidence refs from files, symbols, commits, or prior context packs. When the evidence changes, Contextful marks the memory stale.
Contextful is an MCP server, local indexer, and small CLI:
cxf): human debugging, reports, memory writes, and local smoke tests.MCP is the right interface because tools, resources, and prompts are exactly what MCP standardizes. The agent asks for context; Contextful returns compact evidence.
npx @inferensys/contextful index --workspace .
npx @inferensys/contextful query "where is user auth handled" --workspace . --budget 2000
Run as an MCP server:
npx @inferensys/contextful server
The primary binary is cxf; contextful is also provided as a readable alias.
cxf index --workspace <path> [--watch]
cxf daemon --workspace <path>
cxf query "<query>" --workspace <path> --budget 2000 --json
cxf report --workspace <path> --format markdown|json|html
cxf memory add --workspace <path> --claim <text> --evidence <ref>
cxf server
Keep the agent surface small:
context_pack(query, budget, scope) - the killer tool. Returns a ranked, cited, token-budgeted bundle instead of forcing 40 random file reads.search_code(query, mode, filters) - powerful code, docs, symbol, and memory search.trace_path(from, to, edge_types) - graph traversal across files, symbols, modules, and config.impact_analysis(symbol_or_file) - reverse dependencies and likely tests.why_changed(symbol_or_file) - current evidence plus git history.recall_memory(query, scope) - search session learnings and durable project lessons.write_lesson(claim, evidence_refs, scope) - store an evidence-backed memory.Use this stdio server command in any MCP-aware coding tool:
{
"mcpServers": {
"contextful": {
"command": "npx",
"args": ["-y", "@inferensys/contextful", "server"]
}
}
}
Codex:
codex mcp add contextful -- npx -y @inferensys/contextful server
Contextful reports should make the value visible:
V1 is local-only. It does not call external embedding APIs, upload source code, edit source files, auto-fix code, or install dependencies inside the target workspace.
Memory writes require evidence references returned by search or context packs:
file:src/auth.ts:10-40symbol:src/auth.ts#AuthService:12pack:ctx_...Invalid or stale evidence is rejected or marked stale.
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