ai-market-intelligence
AI Market Intelligence MCP
Ein lokaler MCP-Server in TypeScript zum Vergleich von KI-Modellen nach Szenario: Programmierung, Support, Dokumente, multimodal, Agenten und Datenschutz.
Was diese erste Version tut
listet den lokalen Modellkatalog auf;
empfiehlt Modelle mit expliziten Kriterien zu Kapazität, Kontext, relativen Kosten und eigenem Hosting;
vergleicht ausgewählte Modelle;
erklärt die Gewichtungen, die in jedem Szenario verwendet werden.
Die Profile in src/catalog.ts sind qualitativ und vorläufig: Es handelt sich nicht um offizielle Preise oder Benchmarks. Validieren Sie die Empfehlung mit Bewertungen in Ihrer Domäne und aktualisieren Sie die Quellen, bevor Sie eine Kaufentscheidung treffen.
Related MCP server: LLM Benchmark MCP Server
Ausführen
npm install
npm run build
npm run devZum Testen mit dem MCP Inspector:
npm run inspectVerbinden in VS Code
Erstellen oder aktualisieren Sie .vscode/mcp.json in dem Projekt, das den Server verwenden soll:
{
"servers": {
"ai-market-intelligence": {
"command": "node",
"args": ["C:/Users/gabiv/Projects/ai-market-intelligence-mcp/dist/index.js"]
}
}
}Nachdem Sie npm run build ausgeführt haben, öffnen Sie den MCP-Bereich von VS Code/Copilot und starten Sie den Server. Der Transport ist stdio; daher schreibt das Programm niemals Logs nach stdout.
Nächste empfohlene Weiterentwicklungen
JSON-Datei oder SQLite-Datenbank für Daten und Versionshistorie.
Sammler offizieller Quellen (Preise, Kontextfenster und Grenzen) mit Datum und URL jedes Datensatzes.
Bewertung mit echten Prompts und Metriken Ihres Unternehmens, anstatt sich auf generische Vergleiche zu verlassen.
HTTP-Schnittstelle mit Authentifizierung, falls Sie den Server mit einem Team teilen möchten.
Available Tools
4 toolscompare_modelsComparar modelosB
Compara até quatro modelos para um cenário específico e mostra a classificação.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | ||
| scenario | Yes |
explain_scenariosExplicar cenáriosA
Explica os cenários avaliados e os pesos usados pelo mecanismo de recomendação.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
list_modelsListar modelosB
Lista os perfis de modelos presentes no catálogo local.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No |
recommend_modelRecomendar modeloC
Ordena modelos do catálogo para um cenário, orçamento e requisito de hospedagem própria.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| budget | No | medium | |
| scenario | Yes | ||
| requiresSelfHosted | No |
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
4 tool updates
v0.1.0- First observed
compare_models - First observed
explain_scenarios - First observed
list_models - First observed
recommend_model
TDQS
Scored across 4 tools
The tools are mostly distinct: list_models lists catalog items, recommend_model ranks them, compare_models compares a subset, and explain_scenarios describes the reasoning. However, recommend_model and compare_models could be confused since both rank/compare for a scenario, though compare_models explicitly limits to four.
All tools follow a verb_noun pattern (list_models, recommend_model, compare_models, explain_scenarios). However, the verbs vary in style - 'list', 'recommend', 'compare' are action verbs, while 'explain' is also action but the noun 'scenarios' differs from 'models' - but the pattern is consistent.
With 4 tools, the set is slightly small for a domain that includes model cataloging, recommendation, comparison, and explanation, but it is reasonable for a focused utility. Each tool serves a distinct function and the count is acceptable, though a bit sparse.
The domain appears to be model recommendation and comparison, and the tools cover listing, recommending, comparing, and explaining the logic. However, there is no tool to get details of a single model or to manage the catalog (add/update/delete), which are notable gaps for a complete surface.
Maintenance
Related MCP Connectors
Find AI models that fit a job from your requirements, sourced benchmarks and real cost.
Live prices, rankings, licences and privacy of AI models: best model per task, costs, alternatives.
Find open-weight AI models that fit your hardware, with weight sizes, memory and sources.
Find the right AI model for your job. Free discovery and ranking; tests on your own key.
Related MCP Servers
- AlicenseDqualityDmaintenanceDescribe your AI use case in plain English, get ranked model recommendations with cost estimates and tradeoff reasoning. Covers 62 models across 29 providers. Available as a web app (BYOK + guest tier) and as an MCP server for Claude Desktop and Cursor — same recommendation engine, two interfaces.11MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to compare LLM benchmarks, get pricing, and receive model recommendations for tasks and budgets.1MIT
- AlicenseAqualityDmaintenanceEnables AI agents to discover, compare, and select the best AI models across multiple providers based on pricing, performance, and capabilities, with real-time cost estimation and benchmarking.93,909 npm1MIT
- AlicenseNot gradedqualityDmaintenanceEnables comparing outputs of 17 large language models side by side, with AI judging, cost and speed analysis.676 npmMIT