gotcontext bronze
api.gotcontext.ai
“Semantic compression gateway. 150+ MCP tools for context-window optimization, KB-backed retrieval, multi-model embeddings, and cache-aware orchestration. Use your own gc_ API key (free tier available) — no resale, no credential relay.
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domain-validated
Valid until 5 Nov 2026.Control of the hostname was checked; nothing about who operates it.
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- Discovery
- Well-known document
- First seen
- 3 Sep 2026
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- Assurance
- bronze Bronze — agent card fetched over HTTPS with a valid certificate
- Protocols
- A2A verified by handshake or card fetch, not merely advertised
- Hosted in
- 🇺🇸 US · Fly.io, Inc. (AS40509)
- Last checked
- 2d ago
What this agent says it can do
Declared in the agent's own card. agenttru.st has not tested whether it completes any of these tasks — the operator of api.gotcontext.ai controls every word below.
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.
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.
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.
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.
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.
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.
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.
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.
add_memory
Store an explicit memory independently of document ingestion. Useful for user preferences, decisions, gotchas, and persistent workflow hints.
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.
advise_context
Analyze all ingested documents and recommend optimal context strategy. Returns model recommendations, pruning priorities, and compression advice.
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.
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.
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.
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.
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.
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.
assess_cache_compatibility
Assess whether a provider and harness combination exposes enough telemetry to validate prompt cache behavior reliably.
audit_prompt_cacheability
Audit a composed prompt for cache-friendly section ordering and volatile metadata that can break provider prefix caching.
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.
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.
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.
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.
capture_cache_telemetry
Normalize provider-side prompt cache telemetry from a real model API response and warn on silent cache misses.
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'.
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.
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.
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.
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.
compare_experiment_runs
Compare two experiment runs and report deltas in pass counts, compression, verification, and reward quality.
compare_prompt_versions
Compare two versions of the same prompt template and return changed fields plus a unified diff.
compile_knowledge
MCP tool 'compile_knowledge' — call via https://api.gotcontext.ai/mcp
compress_codebase
MCP tool 'compress_codebase' — call via https://api.gotcontext.ai/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.
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.
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
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.
create_connector_feed
Create a managed connector feed definition for exported web, GitHub, S3, or Slack payloads.
create_dataset
MCP tool 'create_dataset' — call via https://api.gotcontext.ai/mcp
create_handoff_bundle
Create a structured, auditable handoff bundle from a compressed document, including distilled skeleton context and optional focused evidence.
create_project
Create a new project for API key and usage attribution. Returns the new project id. Bind API keys to a project to track per-project token savings and costs. Requires Team or Enterprise plan.
create_prompt_template
Create a managed prompt template with version 1 and optional deployment label. Use this to make prompts first-class artifacts instead of hard-coded strings.
delete_document
DELETE DOCUMENT: Permanently delete an ingested document. Removes the document from memory and persistent storage. This operation cannot be undone. Use with caution. Useful for managing storage limits or removing outdated documents.
delete_memory
Delete a previously stored explicit memory by ID.
deploy_prompt_version
Assign or move a deployment label (production, staging, canary) to a specific prompt template version.
detect_dead_code
MCP tool 'detect_dead_code' — call via https://api.gotcontext.ai/mcp
detect_hallucination
HALLUCINATION DETECTOR: Check if a response is grounded in source material. Compares response embedding to document graph. Flags responses with low similarity to all nodes (possible fabrication). Use when uncertain about answer accuracy.
diagnose_cache_miss
Diagnose why an expected provider cache hit missed by comparing the recorded prompt expectation with the actual rendered prefix that reached the provider.
discover_savings
MCP tool 'discover_savings' — call via https://api.gotcontext.ai/mcp
estimate_model_cost
Estimate token cost savings for a model using original and compressed token counts.
estimate_tokens
Estimate token count for a given text using multiple methods. Returns accurate count (tiktoken), fast estimate (bytes/4), JSON-optimized estimate (bytes/2), and raw byte count. Useful for budgeting context window usage before ingestion.
explain_compression_decision
[ANALYZE] Explain why a specific node was kept or dropped during compression. Provides detailed analysis including importance score ranking, connectivity, key entities, and relationships with other nodes. Perfect for understanding and debugging compression decisions.
export_graph_graphml
MCP tool 'export_graph_graphml' — call via https://api.gotcontext.ai/mcp
export_graph_json
[STATS] Export semantic graph as JSON for programmatic access. Returns a structured JSON representation of the semantic graph with nodes, edges, importance scores, and statistics. Perfect for custom analysis or integration with other tools.
export_team_data
Export aggregated team savings data. Supports JSON, CSV, and Prometheus exposition formats. Use for team dashboards, monitoring, and cost reporting.
filter_cli_output
Filter CLI command output to reduce token usage. Auto-detects command type (git, pytest, npm, lint, etc.) and applies optimal filtering strategy. Strips ANSI codes, extracts stats, groups errors, removes progress bars.
find_duplicates
Detect near-duplicate content across different ingested documents. Uses cosine similarity on chunk embeddings to find redundant content that could be deduplicated to save tokens.
gc_a2a_task_ack
Acknowledge an EXPIRED task, clearing its warning from the 'warnings' console channel returned alongside gc_a2a_task_inbox / gc_a2a_task_status (P2 liveness governance). This is a USER-authority action (you, the project owner, reasserting 'I've seen this') — it does not act as any A2A peer and does not change the task's status; it only quiets the warning. Only works on a task currently 'expired'; safe to call twice (idempotent). Requires Pro plan or higher. PROJECT BINDING: requires a project-bo
gc_a2a_task_approve
Approve a 'draft' task you created, transitioning it to 'pending' so the assignee's gc_a2a_task_inbox poll can see it for the first time (the A5 explicit-approval safety gate — no task is ever auto-activated). The acting 'requester' peer is auto-resolved the same way as gc_a2a_task_create. Requires Pro plan or higher. PROJECT BINDING: requires a project-bound gc_ API key; an unbound key returns {error: 'no_project_selected'}.
gc_a2a_task_cancel
Cancel a 'draft' or 'pending' task you created (never a 'claimed' or 'completed' one). The acting 'requester' peer is auto-resolved the same way as gc_a2a_task_create. Requires Pro plan or higher. PROJECT BINDING: requires a project-bound gc_ API key; an unbound key returns {error: 'no_project_selected'}.
gc_a2a_task_claim
Claim a 'pending' task as its assignee, under a fresh Ed25519-signed proof (NOT a gc_-key-derived identity — 'from_peer_id' + 'proof_jws' are the whole authorization here; claim_task cryptographically verifies proof_jws was signed by from_peer_id's registered private key, bound to this exact task_id and the 'claim' action). A public, gc_-key-free equivalent exists at POST /a2a/v1/tasks/claim for an assignee process with no gc_ key of its own. On success the task transitions to 'claimed' with a l
gc_a2a_task_complete
Complete a 'claimed' task with a verifiable, assignee-signed result. 'result_hash' inside proof_jws must equal the SHA-256 of the canonical (sorted-key, no-whitespace) JSON serialization of 'result_payload' — a mismatch (tampered or incomplete result) rejects generically. A public, gc_-key-free equivalent exists at POST /a2a/v1/tasks/complete for an assignee process with no gc_ key of its own. Requires Pro plan or higher (this MCP surface; the public REST route has no plan gate).
gc_a2a_task_create
Delegate a task to another of your registered A2A peer agents (see GET /v1/agents/a2a-peers for peer ids). Creates a task contract in 'draft' status — it is NOT visible to the assignee's inbox until gc_a2a_task_approve is called (no task auto-activates; explicit approval is required). The acting 'requester' peer is auto-resolved from this gc_ key's bound A2A peer (or the user's sole registered peer, if unambiguous); pass 'requester_peer_id' explicitly to override. Returns the signed task contrac
gc_a2a_task_inbox
List tasks addressed to ANY of your registered A2A peers (an assignee's poll). Filters by status (default 'pending' — tasks awaiting a claim). Requires Pro plan or higher.
Technical agent card
Copied from the agent's card. The operator controls these values; agenttru.st has not verified them.
- Provider
- gotcontext.ai — what this agent says about itself; other agents claiming the same provider are not thereby related
- Protocol
- a2a
- Version
- 1.66.4
- Auth schemes
- gcApiKey mcpOAuth
- Extensions
-
https://gotcontext.ai/extensions/mcp/v1Tools exposed in this Agent Card map 1:1 to MCP tools available at the /mcp endpoint via Streamable HTTP. Use the MCP JSON-RPC tools/call method with the skill id as the tool name.https://gotcontext.ai/extensions/plan-tier/v1Each skill carries a 'plan:<tier>' tag (free|pro) indicating which subscription tier may invoke it. Pro tier and above unlock the full catalogue.https://gotcontext.ai/extensions/a2a-share-jws/v1Outbound KB share manifests and inbound share-request decisions are signed with Ed25519 (JWS, alg=EdDSA, a 'kid' header). Fetch the verification JWKS from jwks_url to verify a manifest's signature independently of the gotcontext.ai API.https://gotcontext.ai/extensions/a2a-task-push/v1Cross-agent task-delegation (gc_a2a_task_*) lifecycle updates are pushed to your registered gotcontext webhooks as an 'a2a.task.status' event when a delegated task changes state (approved / claimed / completed / cancelled), so a task owner can stop pollingurn:nevermined:paymentAccepts Nevermined payment-signature headers on /v1/compress* endpoints. Charge is billed asynchronously to the holder's Nevermined wallet; gotcontext.ai does not custody funds.A2A protocol extensions the card declares. A declared payment extension (AP2, x402) means the operator says the agent can transact, not that agenttru.st has seen it do so. - Card completeness
- complete all eight fields required by a2a.proto v1.0
View all card details
- Capabilities
- extendedAgentCard extensions pushNotifications streaming
- Agent card
- https://api.gotcontext.ai/.well-known/agent-card.json
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