mcp server · observation history
app.neotic.www/neotic
Observed since 2026-08-30, last seen 2026-10-06. Currently reachable. 5 contract changes recorded, 0 of them breaking.
What this server does
In its own words. Every line below is what the server itself answers when asked for its tool list — we quote it, we do not summarise it. The wording is the operator's, not ours.
| Tool | What the server says it does |
|---|---|
cognitive.allocate_computeneeds: task_structure | Adaptive compute budgeting: select compute tier (Fast Path to Deep Deliberation) and timeout based on EVC. |
cognitive.analogical_transferneeds: source_task_structure_id, target_task_structure_id | Transfer structural strategies across disparate domains via Structure-Mapping Engine (SME). |
cognitive.analyze_communicationneeds: content | Pragmatic communication: audit speech acts against Gricean maxims (Quality, Quantity, Relation, Manner) and detect deception. |
cognitive.arbitrate_temporal_objectivesneeds: long_term_option, short_term_option | Arbitrate short vs long term payoffs using hyperbolic vs exponential discounting and Ulysses pre-commitment contracts. |
cognitive.assess_competenceneeds: task_structure | Epistemic boundary awareness: classify task into KNOWN, KNOWN_UNKNOWN, or UNKNOWN_UNKNOWN (OOD) and track calibration. |
cognitive.audit_evidence_graphneeds: task_structure_id | Audit the evidence graph for a task before issuing final answers. Rejects claims such as 'optimal', 'verified', or 'feasible' when their evidence dependencies are incomplete. |
cognitive.build_evidence_graphneeds: task_structure_id | Build or update an Evidence-Carrying Cognitive Graph for a task structure and solution trace. |
cognitive.causal_analysisneeds: edges, observations, outcome, treatment | Distinguish causal effects (do-calculus) from spurious correlation via backdoor adjustment. |
cognitive.compile_invariant_latticeno arguments | Compile a Dynamic Constraint Lattice (DCL) into algebraic boundaries, conservation laws, and reachability cones. |
cognitive.compose_strategiesneeds: composite_name, strategy_ids | Skill Composition: synthesize a composite multi-stage StrategyIR from primitive strategies. Chains multiple specialized skills (e.g. Graph Coloring + Topological Sort + Allocation) into a compound pipeline with explicit stage transitions and end-to-end verification. |
cognitive.compute_intrinsic_rewardsneeds: actual_state | Compute intrinsic drives: novelty & prediction surprise curiosity, empowerment (channel capacity), and learning progress. |
cognitive.compute_lattice_signatureno arguments | Compute coordinate-free topological invariant signature of a lattice or task. |
cognitive.compute_number_theoryneeds: operation | Number theory: primality, factoring, extended GCD, Diophantine, modular inverse, CRT, combinatorics, Fibonacci. |
cognitive.counterfactual_what_ifneeds: counterfactual_action, factual_trace, intervention_step | Counterfactual engine: evaluate 'What if I had done X instead of Y at step t?' using Pearl's abduction-intervention-prediction. |
cognitive.create_simulated_environmentno arguments | Instantiate and initialize a simulated cognitive environment (spatial_commons, multi_agent_arena, sequential_puzzle). |
cognitive.crucible_stress_testno arguments | Subject candidate trajectories to adversarial algebraic perturbations to calculate Robustness Index (R) and project hardened paths. |
cognitive.evaluate_claim_evidenceneeds: claim | Evaluate support status and confidence for an individual claim with evidence. |
cognitive.evaluate_cooperationneeds: game_type | Multi-agent cooperation: analyze game payoff matrices, compute Nash/Pareto equilibria, and execute reciprocity policies. |
cognitive.evaluate_counterfactual_queryneeds: query_type, task_structure_id | Evaluate a counterfactual query on a plan ('What if capacity drops?', 'What if a route fails?', etc.). |
cognitive.evaluate_generalization_benchmarksno arguments | Evaluate broad generalization across spatial commons, multi-agent arenas, and sequential causal puzzles. |
cognitive.execute_taskno arguments | One-call orchestration: identify â gate â guide â solve â verify â report. Parameters: - task: Dict containing: - task_structure (or loose definition: name, entities, constraints, etc.) - raw (optional): Domain-specific execution payload. If omitted, returns status='guidance_only' with 'recommended_action'='supply_raw' and an 'expected_raw_formats' ob |
cognitive.few_shot_induceneeds: solution_trace, task_structure_id | Few-shot learning: induce a generalized procedural StrategyIR from 1-3 problem traces. Extracts structural invariants (decision ordering, invariant contracts, verification rules) and registers an initial candidate strategy immediately without requiring large training sets. |
cognitive.generate_and_prioritize_goalsno arguments | Autonomous goal synthesis from world deficits, depleted reserves, and exploration frontiers with multi-criteria prioritization. |
cognitive.get_experimentneeds: experiment_id | Retrieve details and benchmark results of an experiment (§24, §69). |
cognitive.get_final_evidence_resultneeds: task_structure_id | Compile a final evidence result listing supporting evidence, assumptions, missing evidence, contradictions, unchecked dependencies, confidence, and invalidation conditions. |
cognitive.get_guidanceneeds: task_structure_id | Retrieve applicable validated strategies for a task (§24, §18). Does NOT return unverified or suspended strategies as trusted guidance. Provides calibrated uncertainty, applicability conditions, and negative transfer warnings. Args: task_structure_id: UUID of the abstract task structure. environment: Environment characteristics. goal: Goal description and me |
cognitive.get_strategyneeds: strategy_id | Retrieve a usable strategy: steps, when to use, when not, evidence summary. Disclosure: you learn WHAT to execute, never HOW the engine induces, verifies, or ranks knowledge (no trust signals, audit, tenants, traces). |
cognitive.get_strategy_reportneeds: strategy_id | Evidence dossier: baseline vs assisted, distribution, CI, failures, last eval. |
cognitive.ground_languageneeds: utterance | Non-LLM compositional semantics: parse utterance into semantic predicates and evaluate directly against WorldState. |
cognitive.hierarchical_planneeds: goal_tasks, initial_state | Decompose high-level goals into milestone subgoals using Hierarchical Task Network (HTN) planning. |
cognitive.identify_taskno arguments | Create or resolve an abstract task structure without storing raw private content (§24). Args: task_structure: Structural representation (entities, constraints, variables, etc.). environment: Environmental context and characteristics. goal: Objective and optimization goals. Returns: task_structure_id, structural_features, and matching existing structures. |
cognitive.induce_morphic_transferno arguments | Discover topological homomorphism between source experience and target problem, transducing solution paths. |
cognitive.inferno arguments | Perform logical deduction (Horn clauses) or exact probabilistic Bayesian network inference. |
cognitive.infer_human_valuesno arguments | Infer human values via Bradley-Terry IRL, detect Goodhart's law / specification gaming, and assess CIRL deference. |
cognitive.inspect_lexiconno arguments | Inspect grounded lexicon acquired via situated interaction (learned vocabulary, concept bindings, confidence). |
cognitive.inspect_self_modelno arguments | Engine self-model introspection: inspect capabilities, domain competence, active subsystems, and safety status. |
cognitive.learn_from_mistakeneeds: execution_trace, strategy_id, task_instance, violations | Online Real-Time Error Reflection & Strategy Patching. When an execution fails, analyzes root-cause constraint violations, synthesizes new exception cases and repair procedures, verifies update against anchor regression, and publishes the patched strategy version in real time. |
cognitive.learn_language_interactionneeds: interaction_type, utterance | Interactive language acquisition: learn word-concept bindings through cross-situational observation, games, and feedback. |
cognitive.learn_world_modelneeds: transitions | Online world model learning: update state transition priors from empirical execution traces. |
cognitive.list_experimentsno arguments | Discovery: list recorded benchmark experiment IDs for cognitive.get_experiment. |
100 further tools not shown.
Current claim
What this server says about itself, as of our last probe. Every value carries the date it was recorded.
| Endpoint | https://www.neotic.app/api/mcp |
| Reachable | yes |
| Auth scheme | open |
| Latency class | 3-10s |
| Protocol version | 2025-06-18 |
| Tools | 140 |
| First observed | 2026-08-30 |
| Last seen | 2026-10-06 |
| Went silent | 2026-10-05 |
| Contract changes | 5 |
| Breaking changes | 0 |
What changed, and when
Each row is a recorded fact. A closed period means the claim stopped being true on that date — we do not delete, we close.
| Claim | Value | Period | Recorded |
|---|---|---|---|
| mcp_reachable | {'auth': 'open', 'value': True, 'transport': 'streamable-http'} | 2026-08-30 – 2026-10-05 | 2026-10-06 |
| mcp_latency_ms | {'bucket': '3-10s', 'method': 'Klassenwechsel erst nach zwei Messungen in Folge'} | — | 2026-09-23 |
| mcp_tool_count | {'value': 140} | — | 2026-09-21 |
| mcp_tools | {'tools': [{'name': 'cognitive.allocate_compute', 'optional': ['stakes'], 'required': [' | — | 2026-09-21 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-09-21T19:11:19+00:00', 'previou | — | 2026-09-21 |
| mcp_tool_count | {'value': 124} | — | 2026-09-20 |
| mcp_tools | {'tools': [{'name': 'cognitive.allocate_compute', 'optional': ['stakes'], 'required': [' | — | 2026-09-20 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-09-20T19:13:38+00:00', 'previou | — | 2026-09-20 |
| mcp_tools | {'tools': [{'name': 'cognitive.allocate_compute', 'optional': ['stakes'], 'required': [' | — | 2026-09-20 |
| mcp_tool_count | {'value': 112} | — | 2026-09-20 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-09-20T13:14:57+00:00', 'previou | — | 2026-09-20 |
| mcp_tools | {'tools': [{'name': 'cognitive.execute_task', 'optional': ['environment', 'goal', 'model | — | 2026-09-20 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-09-20T01:14:43+00:00', 'previou | — | 2026-09-20 |
| mcp_tool_count | {'value': 30} | — | 2026-09-20 |
| mcp_server_version | {'name': 'cognitive-engine', 'version': None} | — | 2026-09-19 |
| mcp_contract_change | {'value': 'compatible', 'compared': {'this_probe': '2026-09-19T19:11:02+00:00', 'previou | — | 2026-09-19 |
| mcp_tools | {'tools': [{'name': 'cognitive.execute_task', 'optional': ['environment', 'goal', 'model | — | 2026-09-19 |
| mcp_reachable | {'auth': 'open', 'value': True, 'transport': 'streamable-http'} | from 2026-08-30 | 2026-09-19 |
| mcp_tool_count | {'value': 28} | — | 2026-09-19 |
| mcp_auth_scheme | {'value': 'open'} | — | 2026-09-19 |
| mcp_protocol_version | {'value': '2025-06-18'} | — | 2026-09-19 |
| mcp_reachable | {'auth': 'Bearer', 'value': True, 'transport': 'streamable-http'} | 2026-08-30 – 2026-09-12 | 2026-09-12 |
| mcp_latency_ms | {'bucket': '1-3s', 'method': 'Klassenwechsel erst nach zwei Messungen in Folge'} | — | 2026-08-31 |
| domain_verified | {'note': 'Registry vergibt den Namensraum nur nach Domain-Nachweis; geprueft wird, ob de | — | 2026-08-30 |
| mcp_latency_ms | {'bucket': '<1s', 'method': 'Klassenwechsel erst nach zwei Messungen in Folge'} | — | 2026-08-30 |
| mcp_reachable | {'auth': 'Bearer', 'value': True, 'transport': 'streamable-http'} | from 2026-08-30 | 2026-08-30 |
| mcp_auth_scheme | {'value': 'Bearer', 'challenge': 'Bearer realm="Neotic MCP", resource_metadata="https:// | — | 2026-08-30 |
Machine access — free, no API key
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