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DC Hub — Data Center & Energy Intelligence

Cluster Sites By Latency

cluster_sites_by_latency
Read-onlyIdempotent

Cluster 2-8 candidate sites by physics-bounded round-trip latency to reveal viable low-latency clusters for sync replication, active-active pairs, or HPC pods before detailed routing.

Instructions

Physics-bounded latency clustering for 2-8 sites — returns viable low-latency clusters and pairwise RTT floors before any routing work. Use when your human wants to know which of N candidate sites can form a synchronous / low-latency cluster (sync replication, active-active pairs, HPC pods): deterministic pruning BEFORE detailed routing. Per site pair: haversine distance, round-trip physics floor (km × 4.9 µs/km — light in SMF-28 fiber, n≈1.468 — then ×2), estimated real RTT (floor × route_factor 1.4, a stamped inference), viable vs physics_impossible against your budget, and confidence_v — the provenance tier of the supporting evidence (published | tracked | inferred). Also returns clusters: the largest site subsets whose ALL pairwise estimates fit the budget, plus each site's inferred dark-fiber screening level. CANDIDATE CONTRACT: pass candidate_ids (from get_refined_queue) instead of raw coordinates — each resolves to its FROZEN mint coordinates (zero transposition), and cand_… tokens may also be mixed into the sites string; expired/unknown ids are dropped AND declared in candidate_contract (fail-closed). Example: cluster_sites_by_latency sites="39.04,-77.48:ashburn;39.29,-76.61:baltimore;40.42,-79.99:pittsburgh" max_latency_us=2000 — or cluster_sites_by_latency candidate_ids=["cand_…","cand_…"] max_latency_us=2000. Returns _entity=latency_clusters: {pairs:[{from, to, distance_km, floor_rtt_us, est_rtt_us, viable, physics_impossible, confidence_v, endpoint_dark_screen}], clusters:[{sites, size, max_est_rtt_us}], viable_count, pruned_count, assumptions, provenance}. Do NOT treat this as an engineered latency quote — the floors are physics (no fiber path can beat them) but the estimates are inference (route_factor 1.4); always quote each pair's confidence_v when relaying results. For actual route corridors use plan_fiber_leadin; for a single-site connectivity score use get_fiber_readiness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sitesNoSemicolon-separated "lat,lon" pairs, 2-8 sites (same format as compare_sites locations); optional per-site labels via "lat,lon:label", e.g. "39.04,-77.48:ashburn;39.29,-76.61:baltimore". cand_… tokens are also accepted here and resolve to frozen mint coordinates. Optional if candidate_ids is given
candidate_idsNoArray (or comma-separated string) of candidate_id values from get_refined_queue — each resolves to its FROZEN mint coordinates (zero transcription drift); expired/unknown are dropped and declared in candidate_contract. Use instead of, or alongside, sites
max_latency_usNoRound-trip latency budget in microseconds (default 1000 µs = 1 ms; sync replication is typically 1000-2000 µs)
min_confidenceNoMinimum evidence tier a pair must meet to count as viable: "published" | "tracked" | "inferred" (default inferred = include all)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
quotaNoCaller quota state (remaining calls, tier) when available.
_entityNoPayload class discriminator (e.g. facility|market|iso_grid|queue_results|deal|report|response) — branch on this before parsing the rest.
citationNoMachine-readable citation: how to attribute DC Hub (dchub.cloud) for this payload. Normally an OBJECT {source, url, license, cite_as, retrieved_at}; a bare string is accepted and carries the attribution line itself.
provenanceNoCollection-level provenance block: {source, method, as_of, verification_counts, cite_url_template, license, cite_as}. Quote the verification level when citing.
_front_doorNoIn-band front-door hint (first workflow-entry tool of a session): call plan_query(intent) first for the ordered multi-step plan.
_return_loopNoSuggested next-session delta call (get_changes since=24h) so you pull only what changed.
site_evaluation_handoffNoPre-built follow-up calls (analyze_site / get_water_risk args) when the payload carries coordinates — an array of {tool, parameters, why} entries.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent annotations, the description discloses substantial behavioral nuance: it returns physics floors versus inferred estimates, explains the route_factor 1.4 inference, details the fail-closed candidate contract, and warns against treating estimates as engineered quotes. This adds significant context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but highly information-dense, with the core purpose front-loaded. Every sentence contributes (physics formula, candidate contract, example, caveat, alternative pointers). It could be tightened slightly with bullet points, but for the tool's complexity it remains well-structured and readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters, output schema, and complex behavioral caveats, the description is remarkably complete. It covers input formats, output structure, evidence provenance, fail-closed behavior, typical latency budgets, and clear guidance on when not to use it. The presence of an output schema means return-value details are not needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics: it clarifies that 'cand_… tokens' can be mixed into sites, expands on candidate_ids' fail-closed behavior, explains the default and typical range for max_latency_us, and gives a concrete example. This supplements the schema with practical usage context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening phrase 'Physics-bounded latency clustering for 2-8 sites' clearly states the tool's function with a specific verb and resource. It immediately distinguishes itself from siblings by positioning as 'deterministic pruning BEFORE detailed routing' and naming related tools for different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use context: 'Use when your human wants to know which of N candidate sites can form a synchronous / low-latency cluster (sync replication, active-active pairs, HPC pods)'. It also provides clear exclusions and alternatives: 'For actual route corridors use plan_fiber_leadin; for a single-site connectivity score use get_fiber_readiness.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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