| name | context-loader |
| description | Load relevant MemMesh context before starting work — searches memory and, for a specific subject, assembles a token-budgeted bundle (profile + behavior patterns + forward predictions + top memories) in one call. Use when beginning a task, switching context, or when project history / past decisions / a subject's profile would help.
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context-loader
⚙️ Requires MemMesh hosted mode. Calibrated prediction and behavior discovery run on the hosted engine — set your mm- API key. On a local / open-source install these tools (memory_predict, memory_build_context) are not registered; if a call returns "unknown tool", tell the user this is a hosted capability and fall back to search / recall for what's already known.
Prime the session with the right memory before you act.
General project/session context
{ "name": "memory_search", "arguments": { "projectId": "<repo>", "limit": 20 } }
Skip only on pure pleasantries; the moment the task is substantive, load first.
A specific subject — the synthesized bundle
When you're about to reason about one entity (a user, contact, account), don't
fire five searches — get the assembled picture in one call:
{ "name": "memory_build_context",
"arguments": { "subjectKind": "user", "subjectId": "<id>", "maxTokens": 2000,
"include": ["profile","patterns","predictions","memories"] } }
This returns the profile, active behavior patterns, forward predictions, and
top memories — with provenance ids — ready to drop at the top of your prompt.
That prediction section is the differentiator: you enter the task already knowing
what the subject is likely to do next.
Anticipatory follow-on
Once you're working with a few memories, pull what's most likely needed next via
spreading activation over the graph:
{ "name": "memory_prefetch_related", "arguments": { "seedMemoryIds": ["<id1>","<id2>"], "limit": 10 } }
Cite what you use
When a loaded memory shapes your response, mention it briefly so the user can
correct stale info.
