# FlareSignal
> Provenance-aware Flare and Songbird network, market, XRPFi, yield/capital opportunity, execution, event and Signal Metric intelligence.

FlareSignal exposes structured machine capabilities from the same canonical data core that powers the human site. Machine consumers should use returned timestamps, freshness, source health, methodology, canonicality and evidence references as part of every answer.

## Agent entry point

## Machine interfaces
- [Capability search](https://flaresignal.io/wp-json/flaresignal/v1/machine/discovery/search): Deterministically find the most relevant FlareSignal capability for a natural-language data question.
- [Capability catalogue](https://flaresignal.io/wp-json/flaresignal/v1/machine/discovery/catalog): Browse structured capability semantics, access policy and invocation metadata.
- [Metric catalogue](https://flaresignal.io/wp-json/flaresignal/v1/machine/discovery/metrics): Browse the complete active canonical metric registry with question examples and invocation hints.
- [Metric search](https://flaresignal.io/wp-json/flaresignal/v1/machine/discovery/metrics/search): Search exact metric keys from a natural-language analytical question without using an LLM.
- [Agent broad brief](https://flaresignal.io/wp-json/flaresignal/v1/machine/agent/brief): First-call compact Flare/XRPFi/yield/collateral/market packet for broad questions, with canonical observations and deterministic drill-down routes.
- [Economic ontology](https://flaresignal.io/wp-json/flaresignal/v1/machine/agent/ontology): Machine-only typed relationships between Flare economic concepts; relationships do not imply causality.
- [Agent question catalogue](https://flaresignal.io/wp-json/flaresignal/v1/machine/agent/questions): Browse common machine questions and routes that do not need to appear in the human visual interface.
- [Historical coverage map](https://flaresignal.io/wp-json/flaresignal/v1/machine/history/coverage): Inspect earliest/latest retained observations, observed density and retained coverage state per metric.
- [Advanced history intelligence](https://flaresignal.io/wp-json/flaresignal/v1/machine/history/intelligence): Evaluate dataset coverage quality, gap class, provenance strength, completeness and safe-use decision support; optionally inspect cadence-based expected-vs-observed metric gaps.
- [Yield & capital opportunity catalogue](https://flaresignal.io/wp-json/flaresignal/v1/machine/opportunities/catalog): Browse evidence-backed XRP/FXRP/stXRP/FLR/SGB capital routes, rate evidence, capacity evidence and declared gaps.
- [Yield & capital opportunity search](https://flaresignal.io/wp-json/flaresignal/v1/machine/opportunities/search): Route a natural-language yield, collateral, capacity or earnings question to the relevant opportunity profiles.
- [Opportunity scenario](https://flaresignal.io/wp-json/flaresignal/v1/machine/opportunities/scenario): Model current-rate earnings or mint cost only where an explicit canonical rate metric exists; this is not a forecast or recommendation.
- [Opportunity metric map](https://flaresignal.io/wp-json/flaresignal/v1/machine/opportunities/metric-map): Map the complete active metric universe into commercial yield, capacity, collateral, risk and XRPFi question roles.
- [Opportunity gaps](https://flaresignal.io/wp-json/flaresignal/v1/machine/opportunities/gaps): Return explicit measurement gaps instead of manufacturing APY, rewards or deployable capacity.
- [Machine product catalogue](https://flaresignal.io/wp-json/flaresignal/v1/machine/products/catalog): Browse packaged current and historical intelligence products, packet sections, commercial state and truth boundaries.
- [Machine product search](https://flaresignal.io/wp-json/flaresignal/v1/machine/products/search): Route a research or data-packaging need to the most relevant FlareSignal intelligence product.
- [Product commercial contract](https://flaresignal.io/wp-json/flaresignal/v1/machine/products/commercial): Inspect stable product IDs, packet-unit meter quantities, daily quota policy and current monetary/x402 execution state.
- [Product execution](https://flaresignal.io/wp-json/flaresignal/v1/machine/products/execute): Execute one of the six packaged intelligence products through an authenticated delegated session with required idempotency and durable quota metering. No money is charged.
- [Product usage](https://flaresignal.io/wp-json/flaresignal/v1/machine/products/usage): Inspect the authenticated account daily packet-unit quota and recent metered executions.
- [MCP endpoint](https://flaresignal.io/mcp): First-class stateless MCP 2026-07-28 tools, resources, resource templates and the io.modelcontextprotocol/tasks task extension.
- [OpenAPI](https://flaresignal.io/openapi.json): Generated OpenAPI document from the canonical capability registry.
- [Agent task contract](https://flaresignal.io/wp-json/flaresignal/v1/machine/agent/tasks/contract): Session-bound deterministic research task envelope with stable task IDs, acceptance state, idempotency and trace. The same task store is adapted by native A2A and the MCP Tasks extension.
- [Agent card](https://flaresignal.io/.well-known/agent-card.json): Skill-oriented discovery metadata and supported machine interfaces.
- [Native A2A contract](https://flaresignal.io/machine/a2a): A2A 0.3 HTTP+JSON polling interface, authentication and delegation boundaries.

## Trust model
- Canonical FTSO truth is authoritative where an official Flare FTSO feed exists.
- Verified exchange executions preserve venue-reported direction; missing direction is not inferred.
- Source health, freshness, methodology and evidence are machine-readable.
- Machine responses are audit-linked and deterministically SHA-256 hashed.
- Discovery metadata never expands the underlying capability truth boundary.

## Metric universe
- FlareSignal exposes the complete active canonical metric registry to machine discovery rather than limiting agents to the smaller commercial/API-candidate subset.
- Use the metric catalogue for exhaustive enumeration and metric search for natural-language routing to exact metric keys.
- Each metric discovery row includes safe question examples, units, methodology version and direct live/history invocation hints. Only released public metadata needed for invocation and interpretation is included.

## Agent Intelligence Plane
- Broad/common questions should start with the compact Agent Brief and drill down only when deeper evidence is needed.
- The machine-only economic ontology adds aliases, evidence relationships, comparison candidates and research constraints that would clutter the human visual product.
- Relationship edges are explicitly non-causal unless a separate methodology declares otherwise.
- Historical readiness is a first-class research constraint: agents should use Advanced History Intelligence first, then inspect retained coverage and source evidence before deep historical cross-reference.

## Yield & Capital Opportunity Intelligence
- Opportunity intelligence answers what capital routes exist, which canonical metrics support them, and what rate/capacity/collateral evidence is currently observable.
- Earnings scenarios are calculated only when an explicit canonical APR/APY/rate metric exists and the caller is entitled to that underlying metric.
- Deployable capacity is reported only from an explicit capacity model. TVL, cash or liquidity are never silently treated as a deposit cap.
- FlareSignal does not rank routes by highest yield, make investment recommendations or forecast future returns.
- Missing evidence is surfaced as an explicit gap. FLR/SGB delegation/staking reward rates and native FAssets collateral earnings remain declared gaps until canonical measurement exists.

## Machine intelligence products
- Stable product IDs and packet-unit quantities are published through the product catalogue and commercial contract.
- Product execution requires an authenticated delegated agent session, durable metering and an idempotency key.
- Product responses preserve provenance, freshness, evidence limits and historical-coverage constraints.
- Current monetary prices are not configured, paid intelligence is disabled and product x402 settlement is disabled.

## Machine payment boundary
- The public x402 contract describes the supported machine-payment protocol boundary.
- Product execution does not currently charge money or invoke x402.

## Capabilities
- `flaresignal.agent.access.catalog` - Agent access and delegation catalogue. Describe API-key bootstrap, short-lived delegated agent sessions, read-only scope profiles, TTL limits, live entitlement/revocation policy and the non-delegable generic-surface boundary.
- `flaresignal.agent.brief.query` - Broad Flare intelligence brief. Route a broad natural-language Flare question to a compact provenance-aware packet of entitled canonical observations with deterministic drill-down capabilities and follow-up questions. Example: What is happening on Flare right now?
- `flaresignal.agent.ontology.get` - Flare economic ontology. Expose machine-only concepts, aliases and typed relationships connecting market, XRPFi, yield, collateral, productive capital, liquidity, value capture, events and historical coverage without asserting causality. Example: How are FlareSignal market, XRPFi, yield, collateral, liquidity, value-capture and historical concepts related?
- `flaresignal.agent.plan` - Plan deterministic agent research. Dry-run a evidence-backed research workflow over permanent cross-composition profiles. Returns declared claim/metric dependencies, execution order, propagated entitlements and current historical readiness without executing analytical compositions.
- `flaresignal.agent.questions.catalog` - Agent question catalogue. Browse machine-only common questions and deterministic routes for broad Flare, XRPFi, yield, collateral, market-structure, risk and historical-research intents. Example: What broad and deep questions can an AI agent ask FlareSignal?
- `flaresignal.agent.research` - Execute deterministic agent research. Execute a read-only research plan through the permanent cross-composition engine and return claim-level findings, contradictions, evidence gaps, selected evidence scope and reproducible lineage without re-deriving source metrics.
- `flaresignal.agent.research.trace` - Trace one agent research packet. Return cached read-only lineage for one recent research packet: finding to domain to permanent composition to claim to canonical metric, dataset and source evidence.
- `flaresignal.agent.session.create` - Create delegated agent session. Create a short-lived read-only agent session rooted in an existing FlareSignal API key. Requested scopes may only narrow the parent account authority.
- `flaresignal.agent.session.get` - Inspect delegated agent session. Inspect the current short-lived delegated agent session, its exact capability allow-list, expiry and live parent entitlement state without revealing the session secret.
- `flaresignal.agent.task.contract` - Agent task contract. Describe the deterministic task envelope, acceptance policy, state model, idempotency, session binding, result retention and A2A-adapter readiness without claiming native A2A protocol conformance.
- `flaresignal.agent.task.get` - Get deterministic agent task. Retrieve one recent session-bound task packet, acceptance decision, state history and result without re-executing the research.
- `flaresignal.agent.task.submit` - Submit deterministic research task. Submit one session-bound read-only research task. FlareSignal preflights deterministic profile, delegated scope, live entitlement and historical readiness, then executes only the existing research stack when accepted.
- `flaresignal.agent.task.trace` - Trace deterministic agent task. Trace a recent task from delegated task envelope through research and selected evidence lineage. The trace never broadens the underlying research evidence scope.
- `flaresignal.analytics.catalog` - Analytical capability catalogue. List higher-level FlareSignal analytical capabilities that operate above raw data surfaces and declare their evidence, confidence and historical-completeness contract. Example: Which higher-level analytical capabilities does FlareSignal expose above raw data surfaces?
- `flaresignal.analytics.metric.context` - Metric analytical context. Return an evidence-aware analytical packet for one canonical metric: latest observation, deterministic retained-range statistics, source context, requested-window readiness and explicit historical-completeness constraints without interpolation or prediction. Example: What does this canonical metric look like over 7D, 90D, 1Y or all retained history?
- `flaresignal.audit.contract.get` - Get institutional audit contract. Describe the append-only machine audit ledger, deterministic response hashing, identity/evidence/source-health capture and sensitive-data exclusions applied to machine-ready FlareSignal capabilities. Example: How does FlareSignal make a machine response auditable and evidence-linked?
- `flaresignal.composed.catalog` - Composed Intelligence catalogue. List deterministic multi-dataset analytical compositions, their declared canonical component metrics, input mode, supported ranges/event horizons, claim IDs and truth boundaries. Example: Which deterministic multi-dataset intelligence compositions does FlareSignal expose?
- `flaresignal.composed.query` - Run Composed Intelligence query. Assemble canonical FlareSignal metrics or permanent Event Intelligence checkpoints into evidence-linked conclusion objects with claim-level confidence, actual retained-window coverage and coverage-gated claim voting. Supports nine permanent compositions plus deterministic natural-language routing. Example: Is FLR price appreciation accompanied by improving liquidity, productive utilisation, XRPFi productive capital and value-capture evidence?
- `flaresignal.composed.synthesis.catalog` - Cross-composition synthesis catalogue. List permanent cross-composition profiles, their child composition/claim dependencies, voting domains and truth boundaries. Example: Which permanent cross-composition regime profiles does FlareSignal expose?
- `flaresignal.composed.synthesis.query` - Run cross-composition synthesis. Reuse permanent composition packets to produce transparent cross-domain regime labels with visible domain postures, child claim lineage, no hidden score and optional non-voting Event Intelligence context. Example: Is FLR in a structurally strengthening, weakening or divergent regime across independent evidence domains?
- `flaresignal.discovery.a2a.get` - Get native A2A adapter contract. Describe the native A2A 0.3 HTTP+JSON interface, supported operations, delegated-session authentication, polling task lifecycle and the exact boundary between A2A transport and the existing FlareSignal task/research engine.
- `flaresignal.discovery.agent_access_schema.get` - Get Agent Access JSON Schema. Return the JSON Schema for delegated authenticated agent-session metadata.
- `flaresignal.discovery.agent_card.get` - Get A2A discovery card. Return FlareSignal A2A discovery metadata generated from the canonical capability registry. Example: What FlareSignal skills can another agent discover and what example questions do they handle?
- `flaresignal.discovery.agent_research_schema.get` - Get Agent Research JSON Schema. Return the JSON Schema for deterministic read-only agent research packets.
- `flaresignal.discovery.agent_task_schema.get` - Get Agent Task JSON Schema. Return the JSON Schema for deterministic session-bound agent task packets.
- `flaresignal.discovery.assurance_schema.get` - Get machine assurance JSON Schema. Return the common assurance schema attached to successful FlareSignal machine responses.
- `flaresignal.discovery.capabilities.get` - Get machine capability. Resolve one canonical FlareSignal machine capability definition by permanent capability ID. Example: What are the exact inputs, truth boundary and access rules for a specific FlareSignal capability?
- `flaresignal.discovery.capabilities.list` - List machine capabilities. Enumerate the canonical FlareSignal machine capability registry with stable IDs, truth boundaries, access modes and discovery metadata. Example: What machine capabilities does FlareSignal expose?
- `flaresignal.discovery.catalog` - Browse agent discovery catalogue. Browse capability semantics designed for autonomous agents: assets, domains, intents, example questions, truth boundaries, access policy and invocation metadata. Example: Browse FlareSignal capabilities by asset, domain, topic or access model.
- `flaresignal.discovery.composed_schema.get` - Get Composed Intelligence JSON Schema. Return the JSON Schema for deterministic composed-intelligence packets and claim-level evidence objects.
- `flaresignal.discovery.cross_composed_schema.get` - Get Cross-Composed Intelligence JSON Schema. Return the JSON Schema for transparent cross-composition synthesis packets and domain lineage.
- `flaresignal.discovery.dataset_schema.get` - Get machine dataset JSON Schema. Return the canonical JSON Schema governing FlareSignal machine dataset contracts.
- `flaresignal.discovery.datasets.get` - Get canonical machine dataset. Resolve one permanent FlareSignal machine dataset contract by dataset ID.
- `flaresignal.discovery.datasets.list` - List canonical machine datasets. Enumerate permanent machine dataset IDs for every active canonical metric and canonical historical data family, including schemas, methodology, access and evidence-coverage state.
- `flaresignal.discovery.distribution.get` - Get agent distribution manifests. Return FlareSignal machine-distribution identity and publication artefacts for MCP Registry, x402 Bazaar and llms.txt without claiming third-party publication or indexing. Example: How can agents and registries discover FlareSignal through MCP, x402 Bazaar and llms.txt?
- `flaresignal.discovery.inventory.query` - Query machine inventory. Inspect the current canonical metric, source and Intelligence module inventory without scraping presentation-layer pages. Example: Which metrics, sources and Intelligence modules are registered in FlareSignal?
- `flaresignal.discovery.mcp.get` - Get MCP server contract. Describe the first-class stateless Model Context Protocol endpoint generated from the same FlareSignal capability registry, including protocol version, tools/resources and transport security rules. Example: How can an MCP client connect to FlareSignal and what transport/security rules apply?
- `flaresignal.discovery.metrics.catalog` - Browse complete metric catalogue. Browse every active canonical FlareSignal metric definition with question examples, methodology/accounting metadata and direct machine invocation hints. Example: What metrics does FlareSignal hold across every registered data domain?
- `flaresignal.discovery.metrics.search` - Search registered metrics and answerable questions. Deterministically search the complete active FlareSignal metric registry by natural-language question, asset, domain or concept and return exact metric keys plus safe live/history invocation routes. Example: Which exact FlareSignal metric can answer my natural-language analytical question?
- `flaresignal.discovery.openapi.get` - Get OpenAPI document. Return the generated OpenAPI description derived from the same canonical FlareSignal capability registry. Example: What OpenAPI operations does FlareSignal expose?
- `flaresignal.discovery.parity.get` - Get machine parity audit. Audit every registered FlareSignal GET/read surface against the machine surface registry and report any human-data route without a structured machine equivalent.
- `flaresignal.discovery.schema.get` - Get capability schema. Return the JSON Schema used for FlareSignal machine capability definitions. Example: What JSON Schema defines a FlareSignal machine capability?
- `flaresignal.discovery.search` - Search FlareSignal capabilities. Deterministically rank FlareSignal capabilities for a natural-language data question using curated capability semantics and synonyms; no LLM is used and the search does not answer the underlying intelligence question. Example: Which FlareSignal capability can answer my natural-language data question?
- `flaresignal.discovery.surface_schema.get` - Get machine surface JSON Schema. Return the canonical JSON Schema governing FlareSignal human-to-machine structured surface contracts.
- `flaresignal.discovery.surfaces.get` - Get structured surface contract. Resolve one FlareSignal human-product read surface contract, including its source route, machine route, input schema and original permission boundary.
- `flaresignal.discovery.surfaces.list` - List structured human-to-machine surfaces. Enumerate every structured GET surface used by the FlareSignal product with a permanent surface ID, schema and permission-preserving machine equivalent.
- `flaresignal.events.archive.query` - Permanent Event Intelligence archive. Query the permanent classified Event Intelligence archive with provenance and outcome-state references through the common machine envelope. Example: What significant FLR/SGB events occurred in a historical period?
- `flaresignal.events.live.query` - Live intelligence events. Query significance-ranked current FlareSignal events from the canonical live-event ledger. Returned event direction, actor and protocol context are evidence-backed and are not reconstructed by the machine layer. Example: What significant evidence-backed FlareSignal events are happening now?
- `flaresignal.flow_atlas.stream` - Canonical Flow Atlas stream. Query the structured Flow Atlas composite stream used by the live visual: verified market executions, evidence-backed live events, verified wallet transfers and explicitly labelled measured state changes. The machine layer does not infer geography, trade side or protocol transfers beyond the stored evidence. Example: What verified trades, evidence-backed events and wallet transfers are currently moving through Flow Atlas?
- `flaresignal.history.availability.query` - Historical packet availability map. Expose verified monthly historical availability with canonical dataset identity plus canonical metric-history coverage so agents can assess what evidence is available before requesting a research packet.
- `flaresignal.history.coverage.query` - Historical metric coverage map. Query retained metric history and, on request, canonical dataset readiness with alias resolution, earliest/latest evidence bounds, completeness where defensible, explicit gaps and source-bounded semantics. Example: How much retained history does FlareSignal have for this metric?
- `flaresignal.history.intelligence.query` - Advanced historical intelligence. Classify canonical dataset history by coverage quality, explicit gap type, provenance strength, deterministic evidence confidence, completeness and safe-use decision support. Optionally includes cadence-based expected-vs-observed metric gap reasoning without manufacturing missing observations. Example: Is this historical dataset research-ready?
- `flaresignal.intelligence.catalog.get` - Intelligence capability catalogue. Discover the current FlareSignal Intelligence modules, sections, access boundaries and readiness state as structured metadata rather than scraping the navigation or rendered page HTML. Example: Which FlareSignal Intelligence modules and sections exist and what are their access/readiness states?
- `flaresignal.market.executions.latest` - Verified market execution tape. Query retained verified/derived FLR and SGB exchange executions used by Flow Atlas. Venue-reported side is preserved and never inferred when absent. Example: What verified FLR or SGB exchange executions have occurred recently?
- `flaresignal.market.history.query` - Market chart history. Query canonical FTSO price history joined to the configured reference-venue volume evidence through the common machine envelope. Empty intervals remain empty and no price interpolation is manufactured. Example: How has FLR or SGB canonical FTSO price changed over a selected historical window?
- `flaresignal.market.truth.live` - Current canonical market truth. Return the current official FTSO-led FLR/SGB/XRP market truth through the common FlareSignal machine envelope, including source health, evidence timestamps and exact 24-hour voting-round change context. Example: What is the current canonical FLR or SGB price according to Flare FTSO?
- `flaresignal.metrics.history.query` - Canonical metric history. Query retained canonical observations for one registered FlareSignal metric through the common machine envelope. Entitlement and public/Pro history-depth boundaries remain identical to the human site. Example: How has a registered FlareSignal metric changed over time?
- `flaresignal.metrics.live.query` - Current live metric packet. Query current registered FlareSignal metric observations through the common machine envelope. Each metric remains governed by its own entitlement, methodology, source timestamp and freshness state. Example: What is the current value and provenance of a registered FlareSignal metric?
- `flaresignal.opportunities.catalog` - Browse Yield & Capital Opportunity catalogue. Browse evidence-backed XRPFi, FXRP/stXRP yield, native FLR/SGB staking/delegation and collateral opportunity profiles without ranking by return. Example: Which XRP/FXRP/stXRP/FLR/SGB capital routes can FlareSignal currently observe?
- `flaresignal.opportunities.gaps` - Get Yield & Capital evidence gaps. Return explicit evidence gaps that prevent FlareSignal from safely quoting comparable yield, deployable capacity or FLR/SGB collateral earnings for selected protocols and native-capital routes. Example: Which yield or collateral questions can FlareSignal not answer safely yet?
- `flaresignal.opportunities.metric_map` - Map all metrics to commercial questions. Classify every active canonical FlareSignal metric by Yield & Capital opportunity relevance, theme and question role while leaving the metric definition and entitlement unchanged. Example: Which FlareSignal metrics directly support yield, XRPFi, collateral or capital-allocation questions?
- `flaresignal.opportunities.scenario` - Model current-rate Yield & Capital scenario. Model a stated amount and horizon only where an explicit current canonical APR/APY/fee metric exists; preserve underlying entitlements and report capacity/gaps without making a recommendation or forecast. Example: What would a stated amount earn over a stated horizon if the currently observed explicit rate persisted?
- `flaresignal.opportunities.search` - Search Yield & Capital opportunities. Deterministically route a natural-language capital, yield, FXRP, staking, delegation or collateral question to the relevant FlareSignal opportunity profiles and underlying metrics. Example: Where could a stated XRP-derived asset be productively deployed based on FlareSignal evidence?
- `flaresignal.payment.x402.canary` - x402 settlement canary. Non-intelligence paid canary used only to prove FlareSignal x402 challenge, client signature, facilitator verification, post-handler settlement, receipt, replay protection, metering and audit linkage for isolated settlement verification. Example: Can this agent prove FlareSignal x402 challenge, verification, settlement, receipt and audit linkage without purchasing intelligence?
- `flaresignal.payment.x402.contract` - Get x402 machine-payment contract. Describe the FlareSignal x402 V2 resource-server boundary, accepted network identifiers, provider state, idempotency contract and machine-payment ledger fields. Example: What x402 payment contract, idempotency and receipt rules does FlareSignal use?
- `flaresignal.products.catalog` - Browse machine intelligence products. Browse FlareSignal packaged intelligence products, request/response contract, billing unit, packet sections, truth boundaries and current execution state. Example: What packaged FlareSignal intelligence packets are available to authenticated agents?
- `flaresignal.products.commercial` - Get agent product commercial contract. Return the current quota, meter-unit, replay and execution-state contract for every executable intelligence packet, including whether monetary pricing or x402 settlement is enabled. Example: What packet-unit quantity and quota applies to each executable FlareSignal product?
- `flaresignal.products.execute` - Execute authenticated agent intelligence product. Assemble one declared FlareSignal intelligence product from retained canonical evidence under a delegated-session quota and durable idempotent metering contract. No money is charged and x402 is not invoked. Example: Execute a packaged FlareSignal intelligence packet under the authenticated quota.
- `flaresignal.products.search` - Search intelligence products. Deterministically route a natural-language research or data-packaging need to the most relevant FlareSignal intelligence product contract. Example: Which FlareSignal product matches this requested research packet?
- `flaresignal.products.spec` - Get intelligence product specification. Return one exact FlareSignal product contract including request schema, packet sections, current execution state and truth boundary. Example: What exact request contract, packet sections and truth rules apply to this FlareSignal product?
- `flaresignal.products.usage` - Get authenticated agent product usage. Return the current parent-account UTC-day packet-unit quota, consumption and recent durable product executions for the presenting delegated agent session. Example: How many authenticated packet units has this parent account used today?
- `flaresignal.sources.health.query` - Canonical source health. Query FlareSignal registered source identities and their current health/freshness evidence without scraping Production Runtime or presentation pages. Example: Which FlareSignal data sources are healthy, delayed, stale or failed right now?
- `flaresignal.surface.invoke` - Invoke structured product data surface. Invoke any registered FlareSignal human-product GET surface through the common machine envelope while preserving the exact original permission and entitlement boundary and reusing the same server callback without HTTP loopback.