Skip to main content
Glama

compare_sites

Read-only

Compare 2-4 data center sites side-by-side on grid headroom, fiber, water, taxes, and climate to identify the best location with a recommended pick and decision rationale.

Instructions

Use when a user has narrowed to 2-4 candidate parcels and wants a side-by-side winner picker — grid headroom, fiber, water, tax, climate — with a recommended pick and the reason. Example: "Compare a Phoenix parcel and an Ashburn parcel for a 50MW build — which wins and why?" — compare_sites locations="33.45,-112.07;39.04,-77.48" capacity_mw=50. Params: locations is a semicolon-separated list of "lat,lon" pairs (2-4 max); capacity_mw is the target load (e.g. 50-500). Returns: {sites:[{lat, lon, composite_score, verdict, grid_headroom_mw, nearest_substation_km, fiber_carrier_count, water_stress_score, tax_incentive_value_usd, biggest_risk}], winner:{lat, lon, why}, decision_rationale}. Do NOT use for a single site (use analyze_site) or to rank entire markets (use rank_markets).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationsNo
Behavior4/5

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

Annotations already indicate readOnlyHint=true, so the tool is safe. The description adds value by detailing the return structure (sites array with fields, winner object, decision_rationale) and the decision-making process. No contradictions 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.

Conciseness4/5

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

The description is well-structured: purpose first, then example, parameter details, and sibling exclusions. It is slightly verbose but each sentence adds value. Could be more concise by removing redundant phrasing, but overall effective.

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

Completeness4/5

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

Given no output schema, the description adequately covers the return fields and decision logic. It explains the context of use (candidate parcels, factors) and provides an example. However, it does not explain the meaning of each return field (e.g., composite_score scale, water_stress_score interpretation), which could be helpful.

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?

The input schema has only one parameter (locations) with no description. The description adds format (semicolon-separated lat,lon), constraints (2-4 max), and mentions capacity_mw in the example and param list, but capacity_mw is not in the schema. This inconsistency reduces clarity. Schema coverage is 0%, so the description partially compensates.

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 description clearly states the tool's purpose: comparing 2-4 candidate parcels for site selection. It specifies the factors considered (grid, fiber, water, tax, climate) and distinguishes from sibling tools by explicitly saying when not to use it (single site: analyze_site, market ranking: 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?

Provides explicit when-to-use (narrowed to 2-4 parcels, side-by-side comparison) and when-not-to-use (single site, market ranking). Includes an example query with expected response structure, and names alternatives.

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

Install Server

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