Best for
- Developers who run LLMs locally
- Users needing hardware-aware model selection and Ollama management
- Teams requiring deterministic scoring, calibrated routing, or policy audits
- Users who want structural checks before running downloaded models
L
LLM Checker MCP Server
Community
Choose and manage local LLMs for your hardware.
GitHub source repository ↗
50FMRS · D
The source describes an MCP server that exposes LLM Checker's hardware analysis, deterministic model recommendations, Ollama management, benchmarking, policy auditing, and model-file structural validation. It is suited to local model workflows; the source does not establish that it is an official server from Ollama, MCP, or another upstream product owner.
LLM Checker is a CLI tool with a built-in MCP server. It detects CPU, GPU, memory, and acceleration backends; scores models deterministically across quality, speed, fit, and context; and recommends models for local inference. Through Ollama it can search, download, run, and remove models. It also supports a multi-source model registry, benchmarking, policy auditing, calibrated routing, project recommendations, and structural safety validation for GGUF and safetensors files.
Tools
hw_detect
Detect CPU, GPU, memory, and acceleration backends.
check
Run full hardware compatibility analysis and rank models.
recommend
Recommend models by category.
installed
Rank downloaded Ollama models by compatibility.
search
Search the Ollama model catalog with filters.
smart_recommend
Generate advanced recommendations using the full scoring engine.
gpu_plan
Plan single- or multi-GPU model placement and runtime settings.
verify_context
Check a local model's practical context limit against available memory.
ollama_plan
Compute safe Ollama capacity and runtime settings for local models.
ollama_plan_env
Return ready-to-paste Ollama environment variables.
policy_validate
Validate a policy file against the v1 schema.
audit_export
Export policy compliance reports for check or recommend flows.
calibrate
Generate calibration results and routing policies from a prompt suite.
verify_model
Perform structural safety validation of GGUF or safetensors files with ModelVet.
amd_guard
Run AMD/Windows reliability checks and provide mitigation guidance.
toolcheck
Test tool-calling compatibility for local Ollama models.
ollama_list
List downloaded models with parameters, quantization, family, and size.
ollama_pull
Download a model from the Ollama registry.
ollama_run
Run a prompt against a local model and show tokens/sec metrics.
ollama_remove
Delete an Ollama model to free disk space.
ollama_optimize
Generate Ollama optimization environment variables for the hardware.
benchmark
Benchmark a model with three standard prompts and measure runtime metrics.
compare_models
Compare two models side by side on the same prompt.
cleanup_models
Analyze installed models for redundancies, oversized models, and cloud-only models.
project_recommend
Scan a project and recommend a model for its codebase.
ollama_monitor
Show system status, loaded models, and memory headroom.
cli_help
List CLI commands exposed through MCP.
cli_exec
Execute allowlisted LLM Checker CLI commands.
Setup
Install Node.js 18+ and Ollama, then run npm install -g llm-checker. Use llm-checker mcp-setup --client <client> to print or apply client configuration, or add the supplied npx configuration to an MCP-compatible client. Restart the client after changing its configuration.
claude_desktop_config.json
{"mcpServers":{"llm-checker":{"command":"npx","args":["--yes","--package","llm-checker","llm-checker-mcp"]}}}
Fit and risk
Not for
- Users without Node.js or local Ollama
- Cloud-hosted inference workflows rather than local inference
- Complete guarantees about model behavior, provenance, or poisoned weights
- Arbitrary system command execution beyond the MCP allowlist
Required permissions
- Read local CPU, GPU, memory, and acceleration information
- Access local Ollama model directories and the running Ollama environment
- Depending on the tool, download, run, or remove models and execute prompts
- Read specified project directories or model files for project recommendations and verification
- Execute allowlisted LLM Checker CLI commands
Risks and side effects
- ollama_pull downloads models and consumes network bandwidth and disk space.
- ollama_remove deletes local models and the data they occupy.
- ollama_run and cli_exec can run local model or CLI workflows, so prompts and arguments should be reviewed.
- Structural acceptance only indicates that a file is structurally safe to load; it does not establish model behavior, provenance, or absence of poisoned weights.
- Files above 3 GiB may exceed the WASM32 verification memory ceiling and produce no verdict.
Troubleshooting
- Confirm Node.js 18 or newer and verify that Ollama is installed.
- If database commands report missing sql.js, reinstall with npm install -g llm-checker --include=optional.
- Run llm-checker hw-detect to inspect hardware detection, then run sync to refresh the Ollama catalog.
- Verify that the MCP configuration uses npx --yes --package llm-checker llm-checker-mcp, then restart the client.
- For model failures, check Ollama availability, model installation, available memory, and disk space.
Use cases
Select coding, reasoning, general, or multimodal models for local hardware
Manage, run, benchmark, and compare local Ollama models
Search model artifacts from Hugging Face, Ollama, and GPT4All
Generate hardware-aware Ollama runtime settings
Validate GGUF or safetensors structure before loading a model
Supported clients
Claude CodeFull support
OpenAI CodexFull support
GrokFull support
Kimi CodeFull support
CursorFull support
WindsurfFull support
Gemini CLIFull support