Skip to main content
Glama
azmartone67

DC Hub — Data Center & Energy Intelligence

Predict Market Trajectory

predict_market_trajectory
Read-onlyIdempotent

Forecast a data center market's power trajectory for the next 1-8 quarters. See whether excess power and constraints trend toward BUILD or AVOID, with confidence bands that widen over time.

Instructions

Forecast a DCPI market's near-term trajectory (next 1-8 quarters). Projects excess_power_score and constraint_score forward with confidence bands that WIDEN with horizon, from DC Hub's daily DCPI snapshot history — the only source that can, because it owns the time-series. Use to answer "is this market trending toward BUILD or AVOID?" or "will Dallas power stay tight over the next 6 months?". Params: market_slug (required, metro slug e.g. dallas, phoenix, northern-virginia — valid slugs come from rank_markets / get_market_dcpi_rank); horizon_quarters (optional 1-8, default 4; 2 = ~6 months out). Returns {market_slug, method, basis{history_points, history_span_days, slope_per_day, trend}, horizon_quarters, projection[{quarter_out, excess_power_score, excess_power_band, constraint_score, constraint_band}], caveat, snapshot_record}. HONEST: linear trend extrapolation, NOT a guarantee — bands widen with horizon and short history; needs >=3 daily snapshots or it declines. Do NOT use for a single point-in-time verdict (use get_market_dcpi_rank) or to rank many markets (use rank_markets).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
market_slugYesMarket slug (metro), e.g. dallas, phoenix, northern-virginia — valid slugs come from rank_markets / get_market_dcpi_rank
horizon_quartersNoForecast horizon in quarters (1-8, default 4); 2 = ~6 months ahead

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.
Install Server

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds substantial behavioral context: linear trend extrapolation, confidence bands widening with horizon, minimum 3 daily snapshots requirement, and the data source (DC Hub daily snapshot history). It honestly discloses that the forecast is 'NOT a guarantee.' No contradiction with annotations.

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

Conciseness5/5

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

Although long, every sentence carries functional weight: purpose, when-to-use, parameter breakdown, return shape, honesty/caveat, and exclusions. The core purpose is front-loaded and the structure is logical, moving from high-level capability to specific constraints.

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?

Given the tool's moderate complexity (2 params, output schema), the description covers purpose, prerequisites (valid slugs and minimum snapshot history), parameter semantics, return structure, limitations, and alternative routing. Nothing an agent needs to correctly invoke or interpret this forecast tool is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema fully documents both parameters. The description repeats the same parameter information (valid slugs source, horizon range/default) without adding new meaning beyond the schema. Per the baseline for high-coverage schemas, this is a 3.

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?

Description opens with a specific verb and resource: 'Forecast a DCPI market's near-term trajectory (next 1-8 quarters)' and names the projected metrics (excess_power_score, constraint_score). It also differentiates from siblings by explicitly stating it is not for point-in-time verdicts (use get_market_dcpi_rank) or market ranking (use rank_markets).

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?

Includes explicit when-to-use examples ('is this market trending toward BUILD or AVOID?', 'will Dallas power stay tight...') and clear when-not-to-use guidance with named alternatives. This is the strongest possible guidance: it tells the agent both the conditions and the sibling tools to choose instead.

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

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/azmartone67/dchub-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server