MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
FitLLM is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
3
tools declared
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trust score
remote
at its publisher
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calls last 7d
What it does
Will this LLM fit on your GPU, multi-GPU rig or Mac? Exact VRAM & KV-cache math. Read-only.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --transport http fitllm-mcp https://fitllm.run/api/mcp
# 2 — ask your agent for something these tools do
# check_llm_fit, what_fits_on_hardware, list_supported
FitLLM costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
FIAll 1 server
fitllm1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. Nothing here runs on your machine and nothing runs on ours: this server answers at its publisher’s own address, and what you install is the connection straight to it.
Claude Codedeclared by this listing
This is a public server: it answers at its publisher’s own address and this marketplace is not in the path. Claude Code registers it in one command; --scope user makes it available in every project. The transport below is http, as the publisher's own manifest declares it.
claude mcp add --scope user --transport http fitllm-mcp https://fitllm.run/api/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it answers at its publisher’s own address: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it writes
One config entry pointing straight at the publisher’s own address — this marketplace is not in the path, and nothing from this listing executes on your machine.
What you approve
3 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
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 <model> on my <GPU/Mac>?", "will <model> fit in <N>G
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | LLM name, fuzzy — e.g. "GLM-4.7-Flash", "gpt-oss-20b", "gemma 31b" |
| gpu | string | 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. |
| gpu_count | integer | 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 | integer | no | Apple Silicon unified memory in GB — e.g. 16, 64, 512. Provide gpu OR mac_ram_gb. |
| quant | string | no | Weight quantization. GPU: Q4_K_M(default)/Q5_K_M/Q6_K/Q8_0/FP16. Mac: 4/8(default)/16 (bits). |
| context_tokens | integer | no | Context length in tokens (default 8192). Alias: ctx (same field as the REST API). |
| ctx | integer | no | Alias of context_tokens — accepted because the REST API uses this name. Do not pass both with different values. |
| kv_bits | number | no | KV-cache quantization bits (default 16 = F16) |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| gpu | string | no | GPU name, fuzzy. Multi-GPU rigs: join with + (e.g. "RTX 5090 + RTX 3090"). Provide gpu OR mac_ram_gb. |
| gpu_count | integer | no | Number of identical copies of the gpu. Default 1. |
| mac_ram_gb | integer | no | Apple Silicon unified memory GB. Provide gpu OR mac_ram_gb. |
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.
No parameter schema on file.
Free
$0 forever
- Every tool — any call limit is its publisher’s, not ours
- No card, and mcprush charges nothing for it
- Runs at its publisher’s own address — this marketplace is not in the path
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install FitLLM or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
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Alert at 80% · then the tool returns a normal MCP error
Used this month: —
—Grade not published
No rule-by-rule report on file for this release
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
This listing is an address its publisher runs, and you connect to it directly — this marketplace is not in the path. The scanner reads packages and public source, and an address is neither: the repository linked on this page is where this record was collected from, not a copy of what the address serves, so it is not graded in its place. So there is no grade — not a withheld one, an unmeasured one.
Release history
This is an address its publisher runs: which release it serves is theirs to choose, so there is nothing here to pin. The history below is for reading; the current release on file is 1.1.0.
1.1.0
undated
latestNo release note was published with this version.
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no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish FitLLM can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.