io.github.Daichi-Kudo/llm-advisor
NPM · LLM-ADVISOR-MCP
Real-time LLM/VLM benchmarks, pricing, and recommendations. 300+ models, 5 sources.
Available components
Last scanned (1 hour ago)
Scores higher than 66% of npm servers
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security81
- No malware found by supply-chain analysis.Pass
- CVE check failed: a known high-severity CVE affects zod 3.25.76, a direct dependency. A fixed version is available. View diagnostics → Fail
- No install/post-install scripts declared.Pass
- 30 of 93 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 140 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability79
- AI-judged instruction clarity (excellent).Pass
- Tool/resource definitions use about 467 tokens (~116/item across 4 items; 4 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 4 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 4 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 3 Nov 26 +1
- Stability: 0.97 → pass security
- 2 Nov 26 −1
- Stability: pass → 0.97 functional
- 27 Oct 26 +1
- Stability: 0.97 → pass security
- 26 Oct 26 −1
- Stability: pass → 0.97 functional
- 24 Oct 26 −5
- CVE-2023-54404 affects this package: high ▼ security
- Known CVEs: pass → fail ▼ security
- 20 Oct 26 +1
- Stability: 0.97 → pass security
- 19 Oct 26 −1
- Stability: pass → 0.97 functional
- 17 Oct 26 +2
- Stability: 0.87 → pass security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed npm/llm-advisor-mcp@0.4.5
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Vulnerabilities 1 finding
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-mrhx-c7xc-m3w5 | CVE-2023-54404 | high | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H | yes |
Background: What a vulnerability scan can and cannot prove →
Dependencies 93 packages
| Packages resolved | 93 |
|---|---|
| Stale | 30 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
compare_models ~78
Compare 2-5 LLM/VLM models side-by-side: pricing, benchmarks, capabilities. Returns a compact Markdown comparison table (~400 tokens).
| Name | Type | Req | Description |
|---|---|---|---|
| models | array | yes | Model IDs or partial names (e.g., ["claude-sonnet-4.6", "gpt-5.2", "gemini-3-pro"]) |
No output schema declared.
No examples provided.
get_model_info ~117
Get detailed information about a specific LLM/VLM model: pricing, benchmarks, capabilities, and ready-to-use API code example. Returns structured Markdown (~300 tokens).
| Name | Type | Req | Description |
|---|---|---|---|
| api_format | string | – | API example format (default: openai_sdk) |
| include_api_example | boolean | – | Include API usage code example (default: true) |
| model | string | yes | Model ID or partial name (e.g., "anthropic/claude-sonnet-4.6", "gpt-5.1", "gemini") |
No output schema declared.
No examples provided.
list_top_models ~116
List top-ranked LLM/VLM models for a category. Categories: coding, math, vision, general, cost-effective, open-source, speed, context-window, reasoning. Returns a compact Markdown table (~250 tokens).
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | yes | Category to rank models by |
| limit | number | – | Number of models to return (default: 10) |
| min_context | number | – | Minimum context window in tokens |
| min_release_date | string | – | Minimum release date (YYYY-MM-DD). Excludes older models |
No output schema declared.
No examples provided.
recommend_model ~156
Get personalized model recommendations based on use case, budget, and requirements. Returns top 3 picks with reasoning (~350 tokens).
| Name | Type | Req | Description |
|---|---|---|---|
| max_input_price | number | – | Max input price in USD per 1M tokens |
| max_output_price | number | – | Max output price in USD per 1M tokens |
| min_context | number | – | Minimum context window in tokens |
| min_release_date | string | – | Minimum release date (YYYY-MM-DD). Excludes older models |
| require_open_source | boolean | – | Require open-source license |
| require_tools | boolean | – | Require function/tool calling support |
| require_vision | boolean | – | Require vision/image input support |
| use_case | string | yes | Primary use case |
No output schema declared.
No examples provided.
How do I install the io.github.Daichi-Kudo/llm-advisor MCP server?
io.github.Daichi-Kudo/llm-advisor runs locally as an npm package, launched with npx -y llm-advisor-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · llm-advisor-mcp
claude mcp add daichi-kudo-llm-advisor -- npx -y llm-advisor-mcp
What is the io.github.Daichi-Kudo/llm-advisor MCP server?
io.github.Daichi-Kudo/llm-advisor is an MCP server listed in the public MCP registry as io.github.Daichi-Kudo/llm-advisor. Real-time LLM/VLM benchmarks, pricing, and recommendations. 300+ models, 5 sources. This page covers its npm package (llm-advisor-mcp).
Is the io.github.Daichi-Kudo/llm-advisor MCP server safe to use?
io.github.Daichi-Kudo/llm-advisor scores 80 out of 100 on VerifyMCP. We recorded 1 known advisory against it as of 5 November 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.Daichi-Kudo/llm-advisor MCP server expose?
io.github.Daichi-Kudo/llm-advisor exposes 4 tools: get_model_info, list_top_models, compare_models, recommend_model. Their descriptions and schemas cost roughly 467 tokens of context every time the server is loaded.
Is the io.github.Daichi-Kudo/llm-advisor MCP server still maintained?
io.github.Daichi-Kudo/llm-advisor is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.Daichi-Kudo/llm-advisor MCP server under?
io.github.Daichi-Kudo/llm-advisor declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.