gpu_vram_calculatorGPU VRAM Calculator (Model Size & GPU Fit) — Calculate the VRAM a large language model needs by parameter count and precision (fp16, int8, int4), then see which GPU it fits — from RTX 4090 to H100.
| Name | Required | Description | Default |
|---|---|---|---|
| precision | Yes | ||
| overheadPct | Yes | ||
| paramsBillion | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the core calculation inputs and outcome, but omits the role of overheadPct and does not detail the output format (e.g., whether it returns a single GPU or a list). The precision list in the description is incomplete (missing fp32, bf16), which could mislead. However, it is not contradictory.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that packs relevant information efficiently. It front-loads the action and includes useful parentheticals. It slightly repeats the title at the beginning, but the added specificity (parameter count, precision examples, GPU range) earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema and no annotations, so the description should clarify return values and edge cases. It does not explain overheadPct, the exact meaning of 'fits,' or whether multiple GPUs are returned. For a calculator with three required parameters and an unclear output format, this is insufficient for an agent to confidently invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains paramsBillion and precision, but does not mention overheadPct at all. The precision list is partial and does not match the schema's full enum (fp32, bf16 are missing). No units are specified for paramsBillion beyond the name. This leaves a significant gap for one of three required parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's verb ('Calculate') and resource ('VRAM a large language model needs'), plus the secondary output ('see which GPU it fits'). It distinguishes from sibling calculators by focusing on VRAM and GPU fit rather than cost, throughput, or API usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case: estimating VRAM and GPU fit for LLMs. The context is clear, but it does not explicitly mention alternatives or exclusions, so it falls short of a 5. It provides enough for an agent to know when to select this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.