remem-mcp — Local-first MCP Memory + CodeGraph + Wiki

Get correction learning metrics: total corrections, average precision, heed rate, noise candidates (precision < 0.3), and high-signal candidates (precision >= 0.8). Use this to evaluate memory quality and identify unhelpful corrections to prune.

Explain WHY a memory was recalled for a given query. Shows the BM25 score, vector score, RRF fused score, rank, and matching keywords for each result. Use this to debug unexpected recall results or to understand the retrieval pipeline. If you provide a capture_id, the tool explains why that specific capture was or was not retrieved.

Record a quality signal for a recalled memory. Call this AFTER using a recall result: use 'helpful' if the memory answered your question, 'not_helpful' if it was irrelevant, 'stale' if the information is outdated, or 'wrong' if it is incorrect. This creates a feedback flywheel — useful memories rise, unhelpful ones fade. Do not call this for memories you have not actually used.

Delete specific memory entries. Use this tool only when the user requests a deletion. Do not auto-forget.

Write a structured handoff packet for the next agent session. Call this tool at the end of a session, or before you switch to a different agent. The next agent calls recall to load this packet and continue without re-reading files. This saves 60-85% of tokens compared to re-discovering context.

Diagnose memory server health: DB integrity, index status, capture count, schema version, embedding model.

Register a knowledge asset (wiki or code-graph) for the team. The asset metadata is stored locally. The actual content is processed by an external knowledge service.

Delete one or more knowledge assets by ID.

Get a single knowledge asset by ID.

List knowledge assets for a team. Optionally filter by type.

Update the L3 persona profile for this user/team. Call this when you notice a user preference or pattern (e.g., 'prefers concise output', 'works in Vietnamese', 'uses AZR project'). Persona is injected at SessionStart — every session gets it automatically in ~50 tokens. No LLM needed — you write the trait/value yourself.

Retrieve relevant past memory. Call this tool before you answer the user. Use it when the user references past work or when the task needs project context. Automatically searches both project memory and global cross-project memory (rules, learnings) when REMEM_GLOBAL_SESSION_KEY is configured.

Record whether a correction was heeded (agent followed the advice) or recurred (same error happened again). This tracks correction effectiveness over time. Call after applying a correction to a recalled memory.

Read the raw tool output for a specific canvas node by node_id. Use this to drill down from the Mermaid canvas to the full output when you need details. Requires REMEM_OFFLOAD_ENABLED=true and pipeline=mermaid.

Find memories connected to a given memory by shared tags, project, or co-occurrence. Use this after recall or search to discover related context you might have missed. Inspired by graph spreading-activation (Mnema pattern).

Resolve a conflict between two captures. Mark one as the winner and the other as stale. Call this tool when capture reports a conflict between two memories.

Consolidate multiple L1 atoms into an L2 scenario summary. Call this when you have 5+ atoms about the same topic — it creates a high-signal summary that recall injects in ~100 tokens instead of 5+ individual atoms. No LLM needed — you write the summary yourself based on the atoms you've seen.

Search memory by keyword or by semantic similarity. Use this tool when recall is too broad and you need specific facts. Automatically searches both project and global cross-project memory when REMEM_GLOBAL_SESSION_KEY is configured.

Create a checkpoint of the current session state. Returns a checkpoint ID that can be used to resume later. Stores recent captures as a named snapshot.

Close a session and optionally capture a summary. Call this at the end of a conversation to record what was accomplished.

Open a session and return recent context. Call this at the start of a multi-turn conversation to get a summary of recent captures and correction alignment metrics. Returns both project-specific and global cross-project memory when configured.

Archive a skill — forces it to always be injected into recall, even when the query doesn't strongly match. Use this for critical SOPs that must always be available.

Create a reusable skill (SOP) from a successful task or conversation. Skills are injected into recall when matching trigger conditions are met. Use this after completing a non-trivial task to capture the workflow for reuse.

Get a single skill by ID, including its full content and version.

List skills bound to a team. Optionally filter by agent.

Search skills by keyword. Returns matching skills with descriptions.

Query memory statistics: total captures, breakdown by type, top tags, session count, date range, and database size. Use this to understand memory health and coverage. No arguments needed — returns a summary.

Mark an old memory as superseded by a newer one. The old memory's superseded_by field is set, and it will be filtered out of search results (unless explicitly requested). Use when a fact has changed (e.g. 'database is MySQL' → 'database is PostgreSQL').

Update an existing memory entry. Use this when a capture needs corrections (wrong info, missing tags, needs rewording). Preserves the original ID and created_at.

Get a wiki page by ID, including its links and backlinks. Use this to read a specific page and see what it links to and what links to it.

Ingest markdown documentation files into the wiki. Parses frontmatter, headings, [[wikilinks]], and [text](url) links to build a structured page graph. Supports .md and .markdown files.

Find wiki pages whose source file has changed since the last ingest. Returns pages that need re-ingesting because the source markdown was modified or deleted.

Search wiki pages by content. Returns matching pages with title, file path, and a snippet. Use this to find documentation relevant to a topic.