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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 6 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Wraps arbitrary MCP server tools with metering middleware that intercepts tool invocations without modifying the underlying tool logic. Uses a decorator/proxy pattern to inject usage tracking at the MCP protocol boundary, capturing invocation metadata (tool name, input size, execution time, output tokens) before passing through to the original tool handler. Maintains full MCP protocol compatibility while adding observability hooks for billing calculations.
Unique: Implements MCP-native metering via protocol-level wrapping rather than application-level logging, allowing transparent instrumentation of any MCP tool without code changes to the tool itself. Uses MCP's built-in request/response cycle to capture metrics at the protocol boundary.
vs alternatives: Simpler than building custom billing logic into each tool and more MCP-native than generic HTTP request logging, since it understands MCP tool schemas and can extract semantic usage signals (tool name, parameter types) directly from protocol messages.
Automatically extracts structured usage metrics from each MCP tool invocation, including execution duration, input/output token counts (if applicable), tool name, and invocation timestamp. Aggregates metrics across multiple invocations into usage events that can be exported for billing. Supports custom metric extractors for tool-specific billing dimensions (e.g., API calls made by a tool, database queries executed).
Unique: Extracts metrics at the MCP protocol level, allowing it to understand tool semantics (tool name, schema) and capture usage signals that generic HTTP/RPC logging cannot. Supports pluggable metric extractors for domain-specific billing dimensions without modifying core metering logic.
vs alternatives: More semantic than generic request logging (which only sees bytes/latency) because it understands MCP tool schemas and can extract tool-specific billing signals; more flexible than hardcoded billing logic because extractors are composable and reusable.
Converts metered usage data into billing-ready events that can be exported to external billing systems (Stripe, custom databases, data warehouses). Generates structured billing events with tool usage, metrics, timestamps, and optional customer/tenant identifiers. Supports batch export and streaming event emission for real-time billing pipelines. Events are formatted as JSON and can be written to files, HTTP endpoints, or message queues.
Unique: Generates billing events directly from MCP protocol-level metrics, avoiding the need to instrument billing logic in individual tools or applications. Events are MCP-aware (include tool schema info, protocol metadata) and can be exported to multiple destinations in parallel.
vs alternatives: More integrated than generic usage logging because it understands MCP tool semantics and can generate billing events with tool-specific context; more flexible than hardcoded billing because export destinations and event schemas are configurable.
Provides mechanisms to tag and isolate usage metrics by tenant, customer, or API key, enabling accurate cost attribution in multi-tenant MCP deployments. Supports tenant context propagation through MCP request metadata or custom headers, ensuring each tool invocation is attributed to the correct billing entity. Enables per-tenant usage reports and cost breakdowns without cross-contamination of metrics.
Unique: Implements tenant isolation at the MCP middleware layer, allowing usage to be tagged and segregated without modifying individual tools or requiring tenant-aware tool implementations. Supports multiple tenant context sources (headers, metadata, custom fields) for flexibility in different deployment architectures.
vs alternatives: Simpler than implementing tenant isolation in each tool because it's centralized in the metering middleware; more flexible than hardcoded tenant detection because context sources are pluggable and configurable.
Provides a plugin interface for defining custom metric extractors that can capture tool-specific billing dimensions beyond standard execution time and token counts. Extractors are functions that receive the tool invocation request/response and can compute arbitrary metrics (e.g., number of database queries, external API calls, data volume processed). Extracted metrics are included in billing events and usage reports, enabling fine-grained cost attribution based on tool behavior.
Unique: Provides a composable plugin interface for metric extraction that runs at the MCP protocol boundary, allowing extractors to access both request and response data without modifying tool implementations. Extractors are decoupled from metering core, enabling independent development and reuse across tools.
vs alternatives: More flexible than hardcoded billing logic because extractors are pluggable and reusable; more semantic than generic logging because extractors understand tool-specific behavior and can compute domain-specific metrics.
Enforces usage quotas and rate limits based on metered tool invocations, preventing over-consumption and enabling fair-use policies. Supports per-tenant quotas (e.g., max 1000 tool calls per month), per-tool rate limits (e.g., max 10 calls/second), and custom quota rules. Blocks or throttles tool invocations when quotas are exceeded, returning quota-exceeded errors to the caller. Quotas can be reset on configurable schedules (daily, monthly, etc.).
Unique: Implements quota enforcement at the MCP middleware layer, allowing quotas to be applied uniformly across all tools without modifying individual tool implementations. Supports multiple enforcement modes (blocking, throttling) and custom quota rules for flexible policy implementation.
vs alternatives: More integrated than external rate limiting (e.g., API gateway) because it understands MCP tool semantics and can enforce tool-specific quotas; more flexible than hardcoded limits because quotas are configurable and can be adjusted per tenant.
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs @auvh/climeter-mcp at 21/100. @auvh/climeter-mcp leads on ecosystem, while LlamaIndex is stronger on adoption and quality. However, @auvh/climeter-mcp offers a free tier which may be better for getting started.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities