BM25 Scorer Connector for AI agents.
3 live capabilities
Calculate precise mathematical relevance scores for RAG optimization.
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Why people use BM25 Scorer
Stop wasting API credits with BM25 Context Relevance Scorer
With this Connector, you use mathematical checks. You get a hard score that tells you exactly what stays in the context window.
What Vinkius changes
You get a math-based filter that keeps only the most relevant data in your context window.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Filtering noisy retrieval
You have a vector search returning 20 chunks, but only 3 are useful.
- Real-world use case 02
Cost-effective evaluation
You're testing a new dataset and don't want to pay for GPT-4 calls for every single test case.
- Real-world use case 03
Query expansion analysis
You need to know if adding synonyms helps.
Complete set · 3capabilities
The complete BM25 Scorer capability set.
These are the exact actions your AI can choose when you ask it to work with BM25 Scorer.
01—03
3 capabilities in this set.
Part of 3 available through BM25 Scorer.
- 01 Capability
Calculate term weights
Use this to find the IDF for every term in your query. It tells you which words are unique enough to drive relevance.
- 02 Capability
Analyze document composition
This capability breaks down a document into structural statistics like token counts. It helps you understand the density and frequency of terms within your text.
- 03 Capability
Compute relevance score
This calculates a specific BM25 score for a document against a query. It gives you a hard number to decide if a document stays or goes.
Set up in minutes
One URL. Then ask BM25 Scorer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use BM25 Scorer from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it BM25 Scorer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable BM25 Scorer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the BM25 Scorer URL.
- Step 03
Save and start
Save the connection and enable BM25 Scorer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"bm25-context-relevance-scorer": {
"url": "https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using BM25 Scorer
Open Agent mode in chat and ask: "Using BM25 Scorer, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"bm25-context-relevance-scorer": {
"url": "https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using BM25 Scorer
Ask Copilot: "Using BM25 Scorer, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"bm25-context-relevance-scorer": {
"url": "https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using BM25 Scorer
Open Cascade and ask: "Using BM25 Scorer, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"bm25-context-relevance-scorer": {
"url": "https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using BM25 Scorer
Ask Cline: "Using BM25 Scorer, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add bm25-context-relevance-scorer --transport http "https://edge.vinkius.com/vk_preview_13iaLV42ZXoIPJuXWoZJmsXXcpfHRxpcEFkqQcfP/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using BM25 Scorer
Ask Claude: "Using BM25 Scorer, show me...". 3 tools are ready
Where the request belongs
Work BM25 Scorer can move forward.
AI engineers and data scientists struggling with high RAG costs and noisy retrieval results.
ML Engineer
Checking if new embedding models are actually improving retrieval precision in production.
Data Scientist
Analyzing term frequency and IDF distributions within large text datasets.
LLM Developer
Building automated evaluation pipelines that don't break the bank on API credits.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Cohere (Embed & Rerank)
Empower RAG via Cohere. generate high-quality text embeddings, rerank documents for better accuracy, and perform AI classification directly from any AI agent.
Vector Similarity Threshold Enforcer
Compute exact vector similarity scores and enforce strict relevance thresholds for RAG pipelines.
Linkup (AI Search & RAG)
Power your AI agents with real-time web search via Linkup. execute semantic queries and extract RAG-ready content.
R2R
Equip your AI with direct access to your R2R engine. execute vector searches, run precise RAG queries, and manage your documents.
Bring your own AI
Change the model, client or framework. Keep BM25 Scorer connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about BM25 Scorer.
The practical details behind the request, access and result.
How can BM25 Context Relevance Scorer reduce my RAG costs?
It replaces expensive LLM-based evaluation with mathematical calculations. You stop paying for API calls just to check if a document is relevant.
Can I use BM25 Context Relevance Scorer for semantic search?
Not directly. It focuses on keyword overlap and term frequency. Use it as a second-stage filter after your initial semantic vector search.
Does BM25 Context Relevance Scorer work with any text?
Yes, as long as you provide the necessary corpus statistics or document frequencies to make the math accurate.
How do I integrate BM25 Context Relevance Scorer into my pipeline?
Connect it through Vinkius to your preferred AI client like Claude or Cursor to start scoring documents instantly.
Is BM25 Context Relevance Scorer faster than an LLM?
Significantly. It uses deterministic math instead of neural network inference, making it nearly instantaneous.
How does the scoring work?
The engine uses the BM25 algorithm, calculating IDF based on corpus size and term prevalence, then applying length normalization and saturation constants.
Do I need an LLM to calculate scores?
No, this capability provides deterministic mathematical results using the compute_relevance_score function without any LLM involvement.
What inputs are required for scoring?
You must provide query tokens, document tokens, corpus size, term document counts, and average document length.
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Give your agent a direct line to BM25 Scorer.
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