/recall

Search Kagura Memory Cloud for relevant past knowledge and patterns.

Use the Kagura Memory Cloud MCP tools to search for: $ARGUMENTS

Steps

1. Resolve the target context

list_contexts()

If only one context exists, use it. If multiple, pick the one most relevant to the current project. If unclear, ask the user.

2. Search

Use recall with the resolved context_id:

recall(context_id=..., query="$ARGUMENTS", k=10)

Search modes — choose based on the query type:

• hybrid (default): Best for most queries — combines semantic understanding with keyword matching
• semantic: Use when you know the concept but not the exact words (e.g., "how we handled auth token expiry")
• keyword: Use for exact term matching, hiragana queries, or when semantic returns noise

recall(context_id=..., query="$ARGUMENTS", k=10, search_mode="keyword")

Reranking — enable for higher-quality results when the user has a reranker configured:

recall(context_id=..., query="$ARGUMENTS", k=10, use_rerank=true)

Explore hints — get suggestions for follow-up explore() calls to discover related memories via the knowledge graph:

recall(context_id=..., query="auth token handling", k=10, include_explore_hints=true)

When enabled, the response includes up to 3 explore_hints — each with a memory_id and a reason (top_result, high_centrality, or unexplored_neighbor). Use the suggested memory_id as a seed for explore() to discover related knowledge beyond keyword/semantic matching.

Filters — narrow results by type, tags, importance, date, or source:

recall(context_id=..., query="...", k=10, filters={"type": "decision"})
recall(context_id=..., query="...", k=10, filters={"tags": ["python", "fastapi"]})
recall(context_id=..., query="...", k=10, filters={"tags": ["python", "fastapi"], "tags_match": "all"})
recall(context_id=..., query="...", k=10, filters={"importance": {"gte": 0.8}})
recall(context_id=..., query="...", k=10, filters={"created_after": "2026-01-01T00:00:00Z"})
recall(context_id=..., query="...", k=10, filters={"source_uri_prefix": "vault://my-vault/"})
recall(context_id=..., query="...", k=10, filters={"source_type": "file"})

• tags: matches ANY of the listed tags by default (OR). Add "tags_match": "all" to require all tags (AND). Before building a tag filter, call list_tags(context_id=...) to discover the actual tag spellings in this context — troubleshoot vs troubleshooting drift silently kills tag filters.
• source_uri_prefix: Filter by origin URI prefix (e.g. "file://", "vault://my-vault/"). Useful for querying memories from a specific vault or directory.
• source_type: Filter by origin type — "file" | "url" | "vault" | "api" | "manual".

Cross-context search — to query 2-20 contexts at once, use context_ids instead of context_id:

recall(context_ids=["<uuid-1>", "<uuid-2>"], query="...", k=10)

All listed contexts must:

• belong to the same workspace (otherwise workspace_mismatch)
• share the same privacy setting — all private or all shared, not mixed (otherwise context_privacy_mismatch)
• use the same embedding model (otherwise embedding_model_mismatch)

Filters can be combined:

recall(context_id=..., query="...", k=10, filters={"type": "bug-fix", "tags": ["auth"], "created_after": "2026-03-01T00:00:00Z"})
recall(context_id=..., query="...", k=10, filters={"source_uri_prefix": "vault://", "source_type": "vault", "importance": {"gte": 0.7}})

3. Display results

Show results in a table: memory_id, summary, type, importance, tags.

4. Follow up

• Results found — suggest using reference for detailed content on the most relevant match:

  reference(memory_id=<id_from_results>, context_id=...)
  

• Zero results — try these adjustments:
  1. Shorten or broaden the query (remove specific terms)
  2. Switch search_mode: try keyword if hybrid missed, or vice versa
  3. Remove filters to widen the search
  4. Try related terms or synonyms
  5. Check list_contexts() — the memory may be in a different context