subnet-calculator-mcp
Server Quality Checklist
Latest release: v0.1.2
- Disambiguation5/5
Each tool has a clearly distinct purpose: calculate_subnet and calculate_subnet_from_mask differ by input type, calculate_wildcard_mask is specific, get_nth_usable_ip retrieves a specific IP, and validate_ip_in_subnet checks membership. No ambiguity.
Naming Consistency5/5All tools use a consistent verb_noun pattern with snake_case: calculate_subnet, calculate_subnet_from_mask, calculate_wildcard_mask, get_nth_usable_ip, validate_ip_in_subnet. The verbs 'calculate', 'get', and 'validate' are clear and predictable.
Tool Count5/5With 5 tools, the server is well-scoped for a subnet calculator. Each tool serves a necessary function without redundancy or bloat.
Completeness4/5The set covers core subnet operations (calculation from hosts/mask, wildcard, nth IP, validation). Minor gaps exist, such as no explicit tool for listing all usable IPs or calculating total hosts, but core workflows are supported.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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.
Tools from this server were used 20 times in the last 30 days.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fails to disclose key behavioral aspects: what specific subnet details are returned, input format requirements (e.g., CIDR or dot-decimal), and edge case handling. The phrase 'subnet details' is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no redundancy. However, it could be slightly expanded to include more useful information without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema or annotations, the description is insufficient for an agent to fully understand the tool's capabilities. It lacks information on return values, input validation, and error scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning for network_base and hosts_needed but omits the return_format parameter entirely. Given 0% schema description coverage, the description should have explained all parameters, especially the optional return_format options.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates subnet details using a base IP and required hosts, mapping to the two required parameters. However, it does not differentiate from sibling tools like calculate_subnet_from_mask or specify the exact output details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as calculate_subnet_from_mask or get_nth_usable_ip. The description lacks context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavior. It only states 'Return' without specifying error handling, performance, or that it is a read-only computation. No mention of constraints like valid position range.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence description is efficient and direct, but could be slightly expanded without losing conciseness to include critical context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two required parameters, no output schema, and no annotations, the description omits necessary details like input format, output format, and validation rules, making it incomplete for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should compensate. It only hints at parameters via 'Nth' and 'subnet' but does not specify network format (e.g., CIDR) or position bounds. Adds minimal value beyond the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the Nth usable IP address in a subnet, with a specific verb and resource. However, it does not define 'usable' (e.g., excluding network/broadcast), which could cause ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like calculate_subnet or validate_ip_in_subnet. The description lacks context for appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Description only states the input-output relationship without disclosing what 'network information' includes (e.g., network address, broadcast, usable range), potential validation, or error behavior. Significantly lacking.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, concise but too vague to be effective. Lacks structure and key details that would fit in a brief description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and zero annotations, description should explain what is returned and under what conditions. It only mentions 'network information', leaving the return type and content unspecified. Incomplete for a 2-param tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%. Description mentions the two parameters by name only, adding no additional meaning about format (e.g., dotted decimal vs CIDR) or constraints. The parameter names are self-explanatory but need clarification for correct usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'calculate' and resource 'network information' with inputs (IP address and subnet mask). However, it does not distinguish from sibling tool 'calculate_subnet', which likely has overlapping purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage is that this tool is appropriate when you have an IP and a subnet mask. No explicit guidance on when to use this vs siblings like 'calculate_subnet' or 'get_nth_usable_ip', nor any prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description fails to disclose behavioral details beyond basic validation; 'provide context' is vague without specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise but lacks necessary detail; it is a single sentence but not effective in conveying complete information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description fails to explain what 'context' is provided (e.g., whether gateway is returned), leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explain parameter meanings or usage, relying solely on parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates an IP address in a subnet, distinguishing it from sibling tools that calculate subnets or get nth IPs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like calculate_subnet; lacks explicit context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It states it generates details, but does not specify what details are returned, if there are any constraints (e.g., valid CIDR), or side effects. This is insufficient for a safe selection.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loaded with the purpose. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and zero annotations, the description is too brief. It does not explain output format, constraints, or the optional parameter's effect. Sibling tools exist but are not referenced to aid selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate. The parameters ip_address and cidr_prefix are self-explanatory, but include_ospf_command (default true) is ambiguous—it does not explain what including the OSPF command means. No added value beyond parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates OSPF wildcard mask details for a subnet, which distinguishes it from sibling tools like calculate_subnet (general subnet info), calculate_subnet_from_mask (subnet from mask), get_nth_usable_ip (specific IP), and validate_ip_in_subnet (membership check).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when OSPF wildcard mask is needed, but provides no explicit guidance on when to use this tool versus alternatives, nor any prerequisites or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/sanjayshreeyans/subnet-calculator-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server