Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.
Each tool has a clearly distinct purpose: checking a specific model on specific hardware, listing known names, and recommending models for hardware. The usage examples in the descriptions eliminate any real ambiguity between the model-first and hardware-first tools.
Two tools follow a clean verb_noun pattern (check_llm_fit, list_supported), while what_fits_on_hardware adopts a question-style name. All are snake_case and readable, so the deviation is minor and does not hurt usability.
Three tools is an appropriate size for a focused fit-checking server. Each tool earns its place and together they cover the main interactions a user would need.
The tool surface covers the core domain fully: exact model/hardware fit checks, hardware-directed model recommendations, and supported name lookup. There are no obvious dead ends or missing operations for the stated purpose.
Check whether a specific local LLM fits in the memory of a specific GPU or Apple Silicon Mac. Returns fits/tight/won't-fit verdict with the memory breakdown (weights, KV cache, linear-attention state when present, runtime overhead, reserve), max context, and a concrete fix if it doesn't fit. Use this whenever a user asks anything like "can I run on my <GPU/Mac>?", "will fit in GB?", or "what do I need to run ?". Estimates using curated, config-derived architecture fields (MLA, sliding-window, hybrid attention, MoE modeled).
| Name | Required | Description | Default |
|---|---|---|---|
| ctx | No | Alias of context_tokens — accepted because the REST API uses this name. Do not pass both with different values. | |
| gpu | No | GPU name, fuzzy — e.g. "RTX 4090", "RX 7900 XTX", "A100 80GB". Multi-GPU rigs: join with + — e.g. "RTX 5090 + RTX 3090" (VRAM pools across cards). Provide gpu OR mac_ram_gb. | |
| model | Yes | LLM name, fuzzy — e.g. "GLM-4.7-Flash", "gpt-oss-20b", "gemma 31b" | |
| quant | No | Weight quantization. GPU: Q4_K_M(default)/Q5_K_M/Q6_K/Q8_0/FP16. Mac: 4/8(default)/16 (bits). | |
| kv_bits | No | KV-cache quantization bits (default 16 = F16) | |
| gpu_count | No | Number of identical copies of the gpu (e.g. gpu="RTX 3090", gpu_count=2 for a 2×3090 rig). Default 1. | |
| mac_ram_gb | No | Apple Silicon unified memory in GB — e.g. 16, 64, 512. Provide gpu OR mac_ram_gb. | |
| context_tokens | No | Context length in tokens (default 8192). Alias: ctx (same field as the REST API). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds meaningful behavioral detail beyond that safety profile: the exact return structure (verdict, memory breakdown, max context, concrete fix) and the estimation methodology with modeled architecture types (MLA, sliding-window, hybrid attention, MoE). The phrase 'Estimates using curated...' transparently signals approximation. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four purposeful sentences: scope and purpose, return value, usage triggers, and methodology/limitations. Every sentence earns its place, the main action is front-loaded, and there is no redundancy or filler.
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?
The description is largely self-contained: it covers when to use the tool, what it returns, and key modeling assumptions. Because there is no output schema, the explicit listing of returned fields is valuable. It could be slightly more complete by naming sibling alternatives or addressing edge-case behavior, but the schema and description together provide a solid invocation contract.
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 100%, and the input schema already documents every parameter with defaults, units, aliases, and mutual-exclusion constraints (gpu OR mac_ram_gb). The description adds only high-level framing ('specific local LLM', 'GPU or Apple Silicon Mac') and does not need to compensate for missing parameter documentation. Baseline 3 is appropriate.
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 opens with a specific verb ('Check') and precisely scopes the operation to a specific local LLM against a specific GPU or Apple Silicon Mac, thereby distinguishing it from the broader sibling tools list_supported and what_fits_on_hardware. It also names the concrete verdict categories (fits/tight/won't-fit), leaving no ambiguity about what the tool does.
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 explicitly provides trigger phrases ('can I run...', 'will...fit in...', 'what do I need to run...') and instructs 'Use this whenever', giving the agent clear, actionable context for when to invoke the tool. It does not explicitly name sibling alternatives or state when those tools should be used instead, so it stops short of full when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the built-in model names and hardware names this fit-checker knows (for mapping user wording to exact names). Standard text-only HuggingFace transformer configs can also be checked via fitllm.run; unsupported architectures are rejected.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool read-only, idempotent, and non-destructive. The description adds behavioral context beyond that: the known set is built-in/closed, standard text-only HuggingFace transformer configs are additionally checkable, and unsupported architectures are rejected. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tightly written sentences with no filler. The first sentence front-loads the action and purpose, and the second adds eligibility and rejection behavior without redundancy.
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?
For a zero-parameter, read-only list operation with annotations already covering safety, the description is complete: an agent knows what will be listed, why it exists, and what constraints apply to checkable configs. The return shape is self-ident from the listing semantics.
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?
The tool has zero parameters, so no parameter documentation is needed; the zero-parameter baseline applies. The description still adds useful context about what the returned names are for, which is sufficient.
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?
Clearly states a specific verb ('List'), a specific resource ('built-in model names and hardware names'), and an explicit purpose ('mapping user wording to exact names'). This also distinguishes it from the sibling fit-checking tools, which answer different questions.
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 identifies a clear use context: when an agent needs to map user wording to exact built-in model or hardware names. It does not explicitly name sibling tools or provide when-not-to-use guidance, but the stated purpose makes the boundary sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rank which popular local LLMs fit on a given GPU or Apple Silicon Mac (at ~4-bit quantization, 8K context) — models that fit come first, biggest first, with max context each. Use when a user asks "what can I run on my <GPU/Mac/N GB>?", "best local model for my machine?", or gives hardware without naming a model.
| Name | Required | Description | Default |
|---|---|---|---|
| gpu | No | GPU name, fuzzy. Multi-GPU rigs: join with + (e.g. "RTX 5090 + RTX 3090"). Provide gpu OR mac_ram_gb. | |
| gpu_count | No | Number of identical copies of the gpu. Default 1. | |
| mac_ram_gb | No | Apple Silicon unified memory GB. Provide gpu OR mac_ram_gb. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond that: it assumes ~4-bit quantization and 8K context, ranks fitting models first and largest first, and reports max context per model. This meaningfully informs the agent about how results are produced.
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 two sentences, front-loaded with the core purpose, and contains no filler. Every clause contributes either the behavior, the assumptions, or the trigger conditions for use.
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?
For a simple read-only tool with a fully documented schema, the description covers what the tool does, when to use it, and the shape of the output (ordered list with max context). It does not specify the exact return format, but the absence of an output schema is mitigated by the description's clarity about ranking and included fields.
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 100%, so the parameters are already fully documented. The description adds high-level context by mentioning GPU/Apple Silicon and 'N GB', which maps to gpu and mac_ram_gb, but it does not add detail beyond the schema. Baseline 3 is appropriate.
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 uses a specific verb ('Rank') and a clear resource ('popular local LLMs fit on a given GPU or Apple Silicon Mac'), and it states the ranking criteria. It is clearly differentiated from the likely sibling tools by focusing on hardware-to-model matching rather than checking a specific fit or listing all supported models.
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 explicitly says when to use the tool ('Use when a user asks...') and includes concrete example queries. It does not explicitly mention when not to use it or name sibling alternatives, so it stops short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
check_llm_fit2 fields changedInput schema / properties / context_tokens / descriptionPrevious value: -"Context length in tokens (default 8192)"New value: +"Context length in tokens (default 8192). Alias: ctx (same field as the REST API)."Input schema / properties / ctxAdded value: +{
+ "description": "Alias of context_tokens — accepted because the REST API uses this name. Do not pass both with different values.",
+ "minimum": 1024,
+ "type": "integer"
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Which open models fit your GPU or Mac, measured. Plus model momentum and GPU rental prices.
Will a local LLM run on your hardware? GGUF quant, buy-vs-rent-vs-API cost, used-GPU prices.
GPU and LLM inference benchmarks, hardware evidence, deployment recommendations, and launch configs.
Exact Claude API cost calc with real cache economics, plus a tiktoken-misuse scanner.
Add one secure layer between your agents and this server.
