Last updated: October 10, 2025
Server version:
HOPE Memory
v1.2.0 (Titan aliases remain available)
Tool registry: 17 tools (see below)
You are connected to the @henryhawke/mcp-titan MCP server over stdio. Follow the official tool schemas in docs/api/README.md and prefer the latest help output at runtime.
- Call `init_model` before other memory operations unless the server confirms an active model.
- Use `bootstrap_memory` to seed context before heavy reads.
- Persist state with `save_checkpoint` / `load_checkpoint`; checkpoints must reside in approved directories.
- Monitor capacity via `get_memory_state` and `get_token_flow_metrics`. Prune with `prune_memory` before capacity exceeds 70%.
- Control the learner loop with `init_learner`, `pause_learner`, `resume_learner`, `get_learner_stats`, and `add_training_sample`.
- Treat textual error responses as guidance and adjust parameters accordingly.
| Tool | Purpose | Parameters |
|---|
help
| Lists tools, categories, and optional examples. | { tool?: string; category?: string; showExamples?: boolean; verbose?: boolean; }
|
bootstrap_memory
| Fetches documents from a URL or raw corpus, seeds TF-IDF fallbacks, and stores summaries in memory. | { source: string; } // URL or plain text
|
init_model
| Instantiates HopeMemoryModel
with configurable dimensions and flags. Defaults follow HopeMemoryConfig
. | { inputDim?: number; hiddenDim?: number; memoryDim?: number; shortTermSlots?: number; longTermSlots?: number; archiveSlots?: number; learningRate?: number; dropoutRate?: number; promotionThreshold?: number; surpriseRetention?: number; routerTopK?: number; }
|
| Tool | Purpose | Parameters |
|---|
forward_pass
| Runs a forward pass with optional existing memory state; updates internal memory automatically. | `{ x: string |
train_step
| Performs a supervised update between x_t
and x_next
. Validates matching dimensions. | `{ x_t: string |
reset_gradients
| Clears accumulated gradients to recover from divergence. | {} (no params)
|
add_training_sample
| Pushes samples into the learner replay buffer with optional contrastive pairs. | `{ input: string |
| Tool | Purpose | Parameters |
|---|
memory_stats
| Returns raw memory tensors/stats. Intended for debugging. | {} (no params)
|
get_memory_state
| Summarizes capacity, surprise score, pattern diversity, and quick health check. | {} (no params)
|
get_token_flow_metrics
| Reports token flow window size, weight statistics, and variance when enableTokenFlow
is active. | {} (no params)
|
prune_memory
| Runs information-gain pruning with optional threshold override. | { threshold?: number; force?: boolean; }
|
| Tool | Purpose | Parameters |
|---|
save_checkpoint
| Serializes memory tensors, shapes, and config to a file inside approved directories. | { path: string; }
|
load_checkpoint
| Loads checkpoint data, validating tensor shapes and inputDim
. | { path: string; }
|
| Tool | Purpose | Parameters |
|---|
init_learner
| Starts LearnerService
with configurable buffer/training hyperparameters. Injects mock tokenizer if none present. | { bufferSize?: number; batchSize?: number; updateInterval?: number; gradientClipValue?: number; contrastiveWeight?: number; nextTokenWeight?: number; mlmWeight?: number; accumulationSteps?: number; learningRate?: number; nanGuardThreshold?: number; }
|
pause_learner
| Pauses the learner update interval. | {} (no params)
|
resume_learner
| Resumes the learner update interval. | {} (no params)
|
get_learner_stats
| Returns learner buffer size, step counts, and recent loss metrics. | {} (no params)
|
manifold_step
, encode_text
, get_surprise_metrics
, analyze_memory
, and predict_next
are roadmap items. Avoid calling them until new handlers ship.bootstrap_memory
may fetch external resources; ensure the environment allows network access if required.save_checkpoint
and load_checkpoint
enforce a path allowlist; use absolute paths under ~/.hope_memory
or the working directory.init_learner
installs a random-vector tokenizer by default. Replace server.tokenizer
with AdvancedTokenizer
if deterministic embeddings are needed before enqueuing samples.- For long-running sessions, periodically call
get_memory_state
and prune_memory
to prevent unchecked growth.
await callTool("init_model", { memorySlots: 8000, enableMomentum: true });
await callTool("bootstrap_memory", { source: "https://example.org/notes.txt" });
await callTool("forward_pass", { x: "Summarize the previous meeting notes." });
await callTool("prune_memory", { threshold: 0.75 });
await callTool("save_checkpoint", { path: "~/.hope_memory/checkpoints/session-001.json" });
For schema updates, consult
docs/api/README.md
. For implementation details and roadmap status see
SYSTEM_AUDIT.md
,
IMPLEMENTATION_PACKAGE.md
, and
ROADMAP_ANALYSIS.md
.