Pindoc MCP Server
by var-ggio.github.var-gg/pindocv0.2.0
Code-pinned team memory for AI coding agents — typed artifacts, MCP-native, self-host.
context tax
queued
security
queued
cold start
queued
freshness
Maintained3mo ago
Install Pindoc MCP server
No published package or hosted endpoint yet — see the repository README for build-from-source instructions.
Configuration
| Variable | Required | Secret | Description |
|---|---|---|---|
| PINDOC_DATABASE_URL | yes | — | Postgres connection string. Use the bundled docker compose stack for the recommended pgvector setup. |
| PINDOC_BIND_ADDR | — | — | Daemon bind address. Loopback (127.0.0.1:5830) by default; set a non-loopback value only behind an IdP or trusted proxy. |
Freshness
Maintained — last maintenance signal 3mo ago. The newest of the signals below sets the band.
Last commit (default branch)
2026-07-14 · 3mo ago · GitHub
Latest release
2026-05-05 · 5mo ago · GitHub · v0.2.0
Package published
no data · npm/PyPI
Registry entry updated
2026-05-05 · 5mo ago · official registry · v0.2.0
FAQ
›Does Pindoc require an API key?
Yes. It expects PINDOC_DATABASE_URL.
›Can I use Pindoc as a remote (hosted) MCP server?
Yes. It is a hosted MCP server; connect to its URL with any client that supports remote MCP.
›Is Pindoc in the official MCP registry?
Yes, as io.github.var-gg/pindoc.
Alternatives to Pindoc
Other memory MCP servers.
MemoryActiveKnowledge graph-based persistent memory for maintaining context.
HindsightActiveHindsight: long-term memory for AI agents modeled on human memory.
Codebase MemoryActiveIndexes a repo into a persistent local knowledge graph of functions, classes, call chains, HTTP routes and cross-service links, using tree-sitter and LSP.
CogneeActiveMemory manager for AI apps and agents using graph and vector stores, with ingestion from many data sources.
HonchoActiveMemory that reasons: continual learning for stateful agents. Better context, fewer tokens.
Basic MemoryActiveLocal-first AI memory: a semantic knowledge graph of Markdown files that humans and LLMs both read and write, with optional cloud sync and team workspaces.