datacenter-mcp-server

Server Quality Checklist

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Latest release: v1.1.1
Disambiguation5/5
Each tool addresses a distinct data center engineering concern—cooling, power redundancy, tier assessment, commissioning, rack density, GPU cooling, UPS sizing, and reference lookup. There is no semantic overlap that would cause an agent to select the wrong tool.
Naming Consistency2/5
Most tools follow a verb_noun pattern (dc_calculate_cooling_load, dc_analyze_power_redundancy, dc_generate_commissioning_plan), but three deviate: dc_gpu_cooling_optimizer, dc_ups_battery_sizing, and dc_reference_lookup use noun-based phrasing. This mixed convention undermines predictability.
Tool Count5/5
Eight tools is a well-scoped count for a data center engineering server. Each tool adds meaningful capability, and the set is neither bloated nor too sparse.
Completeness4/5
The server covers core design and analysis tasks including cooling, power, redundancy, tier classification, commissioning, and GPU-specific cooling. Minor gaps exist (e.g., no energy cost or physical security tools), but the domain is well covered.
Average 4.2/5 across 8 of 8 tools scored. Lowest: 3.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds 'Returns reference data tables' and clarifies the tier filter, providing some context beyond annotations. No additional behavioral details like rate limits or error handling are included, but none are critical for this read-only tool.
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 with purpose, Args, Returns, and Examples sections. The Args section somewhat repeats the schema, but the examples justify the inclusion. It is concise enough for the tool's complexity, with no unnecessary filler.
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?
For a simple reference lookup with fully described parameters and safety annotations, the description provides sufficient detail: categories, optional tier filter, return type, and usage examples. Lack of an output schema is acceptable, though more specifics about table format would improve completeness.
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% with descriptions and enums for both parameters. The description goes further by mapping natural language examples to argument values (e.g., 'What are the Tier III requirements?' -> category=tier_requirements, tier=3), enhancing an agent's understanding of how to construct valid calls.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb ('look up') and specifies the resource ('data center engineering reference data'), listing four distinct categories. It distinguishes itself from sibling analysis tools by focusing on reference data retrieval, but does not explicitly disambiguate from overlapping tools like dc_assess_tier_classification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Examples show common query patterns, but there is no explicit guidance on when to use this tool versus the sibling analysis tools (dc_calculate_*, dc_analyze_*). The context of 'reference data' implies use for static lookups, yet exclusions are not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, idempotent, and non-destructive, so the bar is lower. The description adds value by listing the calculations performed and stating it returns structured JSON, but it does not disclose deeper behavioral details such as error handling, prerequisites, or response format specifics.
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?
The description is well-structured with a clear opening purpose, a list of calculations, an Args section, and Examples. Every sentence contributes meaningful information, and the length is appropriate for a tool with seven parameters; there is no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, and while the description lists many outputs and says it returns structured JSON, it does not specify the JSON keys or structure. For a complex tool without an output schema, more detail about the return format would be needed for full completeness, but the description still gives a solid high-level overview.
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?
The input schema already covers 100% of parameters with descriptions and defaults, so the baseline is 3. The description adds value through its Args list (though largely duplicative) and especially the two concrete examples, which illustrate how to map natural language queries to actual parameter values.
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 analyzes power redundancy configuration for data centers and lists specific calculations (UPS module count, generator capacity, PDU requirements, etc.). This verb+resource combination distinguishes it from sibling tools like cooling load or tier classification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by focusing on power redundancy analysis and provides two examples demonstrating common scenarios. However, it does not explicitly state when to use this tool versus alternatives or mention exclusions, so the guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description is consistent with these, and adds valuable context about what the tool computes (CDU sizing, coolant flow, PUE impact). It does not state assumptions or limitations, but annotation coverage lowers the burden.
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 front-loaded with purpose, followed by capability context, a structured Args list, return summary, and examples. It is longer than necessary due to repeating schema details, but the structure is logical and the examples aid usability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is complex (7 params, no output schema), and the description lists return values and gives examples. However, it omits the electricity_cost_per_kwh parameter and lists defaults for two parameters that are required per schema, which could mislead an agent into omitting them. This gap prevents full completeness.
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 covers 100% of parameters with descriptions, so baseline is 3. The description's Args section largely repeats schema info but adds defaults for ambient_temp_f and pue_target (not in schema) and omits electricity_cost_per_kwh. This provides some extra meaning but also introduces inconsistency.
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 uses a specific verb ('Optimize cooling infrastructure') and concretely enumerates outputs: thermal loads, cooling strategy, energy costs/savings. It clearly differentiates from siblings like dc_calculate_cooling_load by focusing on GPU/AI workloads and including cost projection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly scopes the tool to GPU/AI workloads, listing supported GPU models and cooling types, which implies when to use it. However, it does not explicitly compare against sibling tools or mention exclusions, so it lacks explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive. The description adds context about included calculations (IT heat rejection, electrical losses, lighting, humidification, altitude derating) and return format, exceeding the annotation baseline. No contradictions.
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, capabilities, factors, args, returns, examples. It is front-loaded with key info. However, the Args section duplicates the schema, adding redundancy and length without new value.
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?
For a complex tool with no output schema, the description covers return fields and calculation factors, plus examples. It lacks emphasis on required parameters and edge cases, but is otherwise complete for an agent to use correctly.
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 coverage is 100%, so the baseline is 3. The Args section mostly reiterates schema descriptions. The examples add some semantic mapping from natural language to parameters, but not enough to elevate beyond baseline.
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 calculates total cooling load for a data center, with specific outputs (kW, tons, BTU/hr, CFM, PUE rating, recommendations). It differentiates from siblings by focusing on cooling load, while others address power redundancy, tier classification, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context on what the tool does and its inputs (factors like IT load, PUE, environmental factors), with examples of typical use. However, it does not explicitly mention alternatives or when not to use this tool, though sibling tools are distinct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which the description does not contradict. The description adds useful context about what the tool calculates (including lifecycle cost analysis with replacement cycles) and that it returns recommendations, which goes beyond the bare safety 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 with a clear opening purpose, followed by Args, Returns, and Examples sections. It is front-loaded and every major section earns its place, though the Args list largely duplicates schema descriptions, making it slightly longer than necessary.
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 that there is no output schema, the description adequately explains what the tool returns (UPS module sizing, battery string count, floor space, weight, TCO, recommendations) and covers supported configurations and battery types. The examples further clarify expected parameter usage, making the tool fully usable without external documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description's Args section adds little beyond the schema and actually introduces a misleading restriction: 'runtime_minutes (5, 10, 15, or 30)' contradicts the schema's range of 5-120. The examples are helpful for mapping natural language to parameters, but the inaccuracy lowers the value added.
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 with specific verbs ('Size', 'Calculates') and a defined resource ('UPS systems and battery plants'). It lists concrete outputs (module count, battery string sizing, TCO) and explicitly distinguishes itself from sibling tools like cooling load calculators or generic power redundancy analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description establishes clear usage context by specifying the domain (mission-critical data centers), supported redundancy configurations, battery technologies, and typical runtime values. It does not name alternatives or explicit exclusions, but the focus on UPS/battery sizing is unmistakable compared to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to restate those. It adds value by detailing what the tool returns: density classification, cooling strategy, airflow estimates, and thresholds for containment and liquid cooling. This gives the agent a clear picture of the tool's behavior and output beyond the annotation hints.
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?
The description is well-structured with a concise intro, a clear Args list, a Returns line, and illustrative examples. Every sentence serves a purpose, and the overall length is appropriate for the tool's complexity. No filler or redundancy.
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?
The description fully covers what the tool does, the parameters it accepts, and the outputs it produces. With no output schema, the description carries the burden of explaining return values, and it does so clearly (classification, strategy, airflow, recommendations). Examples add practical context, making it complete for an analysis tool.
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?
The input schema has 100% parameter coverage with detailed descriptions, so the baseline is 3. The description goes beyond the schema by providing concrete value examples (e.g., 8 kW typical enterprise, 40+ GPU) and natural-language mapping examples, which adds practical meaning for parameter selection.
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 starts with a specific verb ('Analyze') and resource ('rack density') and clearly states the tool's purpose: to classify density and recommend cooling strategies. This distinguishes it from siblings like dc_calculate_cooling_load or dc_gpu_cooling_optimizer, which focus on different aspects of cooling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool through examples and a returns summary, but it doesn't explicitly state when not to use it or point to sibling alternatives. This is a strong but not explicit usage guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior, so the bar is lower. The description adds useful context: it evaluates specific infrastructure aspects, identifies gaps, and returns target vs achieved tier, gap analysis with severity ratings, uptime expectations, and recommendations. This goes beyond the annotations without contradicting them.
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?
The description is well-structured with a clear overview, evaluation criteria, return value summary, parameter list, and examples. It is appropriately sized for a 10-parameter tool; every sentence adds value and the most important information (purpose) is front-loaded.
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 complexity (10 params, no output schema), the description provides a complete picture: it states what the tool does, what it evaluates, what it returns, and includes two concrete examples. The annotations and full schema coverage cover safety and parameter details, so no critical context is missing.
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 baseline is 3. The description's Args list repeats parameter names and brief meanings, but the examples provide additional semantic value by showing how parameters like power_redundancy, cooling_redundancy, and distribution_paths are combined for realistic scenarios (e.g., 2N facility for Tier IV), which enhances understanding beyond the schema's dry definitions.
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 assesses a data center's Uptime Institute Tier classification based on infrastructure configuration, a specific verb+resource+scope. It distinguishes from sibling tools like dc_analyze_power_redundancy by covering multiple dimensions (power, cooling, distribution, maintainability, fault tolerance) rather than a single aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains what inputs are evaluated and mentions identifying gaps between current infrastructure and target tier, implying when to use it (when a tier classification assessment is needed). Examples show common queries like 'Does my N+1 facility qualify for Tier III?', but it does not explicitly discuss alternatives or exclusions relative to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent annotations, the description discloses meaningful behavioral details: it follows ASHRAE levels, scales to facility size and tier complexity, and returns a structured plan with phases, test procedures, durations, milestones, and recommendations. It also explains the meaning of each commissioning level, providing transparency about what the generated plan contains.
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 and front-loaded with the core purpose. It includes the level definitions, an Args section, and examples. The Args section partially duplicates the schema descriptions, but the overall organization is logical and each section serves a clear role. It is longer than minimal but appropriate for a complex generation tool.
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 there is no output schema, the description appropriately explains the return value ('structured plan with phases, test procedures, durations, milestones, and recommendations'). It also defines the commissioning levels, parameter semantics, and provides examples. This is a complete, self-contained description for an agent to select and invoke the tool correctly.
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?
The input schema already covers 100% of the parameters with descriptions, so the baseline is 3. The description adds value through the commissioning-level definitions and two realistic examples that show how to interpret user phrasing into exact parameter values (e.g., '5 MW Tier III' -> facility_size_kw: 5000, tier_level: 3). This practical mapping goes beyond the schema's static field descriptions.
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 opens with a specific verb and resource: 'Generate a comprehensive data center commissioning plan following ASHRAE guidelines (Levels 1-5).' This clearly distinguishes it from sibling tools like dc_calculate_cooling_load or dc_assess_tier_classification. It also outlines the structured output and scaling behavior, making the tool's purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by explaining what the plan includes and how it scales, and it supplies concrete examples mapping natural-language requests to parameter values. It does not explicitly state when not to use this tool or name alternatives, but none of the sibling tools overlap with commissioning-plan generation, so the usage context is sufficiently clear.
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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