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allenc84
by allenc84

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
OPENAI_API_KEYYesOpenAI API key for embeddings
ANTHROPIC_API_KEYYesAnthropic API key for synthesis
MEMORY_USER_CONTEXTYesUser context string, e.g., 'Jane Doe, founder of Acme'

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
search_memoryA

Search the memory system by semantic similarity. Use this at the start of any topic-specific conversation to surface relevant context, prior decisions, and evolved thinking — without being asked. Returns memories ranked by relevance × salience.

save_memoryA

Save a new memory. Use this to record decisions made, insights surfaced, how the user's thinking has evolved, feedback given, or important context from this conversation.

get_context_briefA

Get a synthesized brief on a topic — what is known, how thinking has evolved, and what open questions remain. Use before deep-diving into any recurring topic like web onboarding tests, the stock portfolio, or an M&A situation.

get_relatedC

Given a memory ID, find semantically related memories. Implements spreading activation.

consolidateA

Run the consolidation job: extract semantic patterns from recent episodic memories. Run this at the end of sessions covering important topics. This is what makes the memory system get smarter over time.

list_memoriesC

List recent memories, optionally filtered by type.

memory_statsA

Return total memory count and breakdown by type.

log_assessmentA

Log a forward-looking assessment to the judgment ledger. Use this whenever making a prediction, recommendation, or forward-looking call. Prefer a numeric 'probability' (0-1) — it enables real calibration (Brier score) over time. Assessments are tracked and later scored against what actually happened.

list_pending_assessmentsA

List unresolved assessments from the judgment ledger. Use during weekly review to surface what needs scoring, or at session start to remind of open calls in a domain.

resolve_assessmentA

Mark an assessment as resolved with its actual outcome and score. score: 1 = right, 0 = partially right, -1 = wrong. Call this as soon as the outcome of a prediction is known.

generate_calibrationA

Extract calibration patterns from resolved assessments in a domain and write a high-salience feedback memory. Run this after a batch of resolutions in a domain. Requires at least 3 resolved assessments in the domain.

get_bias_mapA

Generate a structured bias report across all resolved assessments. Shows where judgment is well-calibrated vs. systematically off. Use for quarterly self-calibration review.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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