Tool Result Caching With Ttl And Invalidation

via “result caching with configurable ttl and eviction policies”

Self-hardening prompt injection detector with multi-layer defense.

Unique: Implements configurable in-memory caching with multiple eviction policies (LRU, LFU, FIFO) and per-request cache bypass options, allowing developers to balance latency, cost, and memory usage; cache key includes configuration state to prevent incorrect hits when settings change

vs others: More sophisticated than simple TTL-based caching by supporting multiple eviction policies and configuration-aware cache keys; reduces API costs for repetitive workloads without requiring external cache infrastructure

via “caching middleware for tool results with configurable ttl and invalidation”

🚀 The fast, Pythonic way to build MCP servers and clients.

Unique: Implements transparent result caching at the middleware level, allowing tools to be cached without modification. Cache keys are derived from input parameters, and TTL/invalidation can be configured per-tool or globally.

vs others: More transparent than tool-level caching because caching is applied via middleware without modifying tool code, and more flexible than application-level caching because cache configuration is centralized in the server.

via “caching layer for tool results and resource content”

Opinionated MCP Framework for TypeScript (@modelcontextprotocol/sdk compatible) - Build MCP Agents, Clients and Servers with support for ChatGPT Apps, Code Mode, OAuth, Notifications, Sampling, Observability and more.

Unique: Integrates caching as a declarative middleware layer that can be applied to any tool or resource without modifying handler code, with pluggable backends (in-memory, Redis, Memcached) and configurable invalidation strategies

vs others: Simpler than manual caching because cache logic is declarative and applied uniformly, whereas per-tool caching requires duplicated logic in each handler and is error-prone

via “intelligent response caching with redis backend and cache invalidation”

An AI Gateway, registry, and proxy that sits in front of any MCP, A2A, or REST/gRPC APIs, exposing a unified endpoint with centralized discovery, guardrails and management. Optimizes Agent & Tool calling, and supports plugins.

Unique: Implements tenant-aware cache isolation by including user/team context in cache keys, preventing cached results from one tenant from being served to another. Supports declarative cache invalidation rules that trigger when specific tools are invoked, enabling eventual consistency without explicit cache busting.

vs others: Unlike simple HTTP caching (which is transport-agnostic but ignores tool semantics), ContextForge's caching understands tool parameters and can invalidate based on tool dependencies, providing higher cache hit rates for complex tool chains while maintaining security boundaries.

via “result-caching-and-ttl-management”

Robust, fast, scalable, and sandboxed open-source online code execution system for humans and AI.

Unique: Caches execution results in Redis with hash-based deduplication, enabling result reuse for identical submissions while automatically expiring results after configurable TTL

vs others: Hash-based caching is simpler than semantic deduplication; automatic TTL expiration prevents stale results; Redis caching is faster than database queries

via “in-memory-caching-with-time-based-invalidation”

MCP-NixOS - Model Context Protocol Server for NixOS resources

Unique: Implements simple time-based caching with configurable TTL (default 1 hour) in ChannelCache and NixvimCache classes, reducing latency for repeated queries without requiring external cache infrastructure. Cache keys based on query parameters enable efficient cache hits.

vs others: In-memory caching with time-based invalidation is simpler than external cache systems (Redis, Memcached) while providing significant latency reduction for typical usage patterns.

via “redis caching strategy with multi-layer cache invalidation”

A repository of models, textual inversions, and more

Unique: Implements a multi-layer caching strategy with different TTLs and invalidation patterns for different data types, optimizing for both hit rate and freshness. Event-based invalidation ensures caches are updated when underlying data changes, reducing stale data issues.

vs others: More sophisticated than simple full-page caching because it caches at multiple layers (API responses, queries, computed values) and uses event-based invalidation, though it requires careful design to avoid stale data.

via “tool result caching and memoization for repeated invocations”

** - A CLI host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP).

Unique: Implements transparent result caching with configurable TTL and backend storage, automatically memoizing tool invocations without requiring tool-specific cache logic

vs others: More flexible than tool-level caching and more maintainable than application-level caching, centralizing cache management and enabling cache sharing across multiple tool invocations

via “tool result caching and deduplication”

MCP tool loader for the Murmuration Harness — connects to MCP servers and converts tools to LLM-compatible format.

Unique: Implements tool-aware result caching with per-tool cache policies, rather than generic HTTP caching, allowing fine-grained control over which tools are cacheable and for how long

vs others: Provides semantic caching based on tool identity vs. HTTP caching headers, enabling cache policies that match tool semantics rather than transport protocol

via “tool result caching and memoization”

LangChain.js adapters for Model Context Protocol (MCP)

Unique: Provides transparent result caching at the adapter layer, allowing agents to benefit from memoization without modifying tool definitions or agent logic

vs others: More efficient than agents that don't cache because repeated tool calls with identical parameters return cached results immediately

via “tool result caching with configurable ttl”

Tools for writing MCP clients and servers without pain

Unique: Transparent tool result caching with configurable TTL and Redis support — intercepts tool calls and returns cached results without modifying tool handler code, with optional distributed cache for multi-instance deployments

vs others: Reduces tool call latency and API costs vs no caching; distributed Redis support vs in-memory-only caching for single-instance deployments

WaniWani SDK - MCP event tracking, widget framework, and tools

Unique: Integrates caching as a first-class concern in the tool execution pipeline with metadata-driven cache policies, rather than requiring developers to implement caching manually in each tool handler

vs others: More maintainable than manual caching in tool handlers because cache logic is centralized and can be updated globally, while remaining simpler than building custom caching infrastructure

via “configurable query result caching with ttl-based invalidation”

** Provides multi-cluster Kubernetes management and operations using MCP, It can be integrated as an SDK into your own project and includes nearly 50 built-in tools covering common DevOps and development scenarios. Supports both standard and CRD resources.

Unique: Provides a simple TTL-based caching layer that integrates transparently with fluent API queries, reducing API server load without requiring explicit cache management; cache keys are automatically derived from query parameters

vs others: Simpler than implementing custom caching logic because it's built-in; more efficient than repeated API calls for read-heavy workloads

via “ttl-based tool response caching for mcp servers”

TTL cache wrapper for MCP tool handlers — powered by vurb.

Unique: Provides MCP-native caching via decorator pattern that wraps tool handlers at registration time, leveraging vurb's abstraction layer to integrate seamlessly with MCP server tool registries without requiring middleware or proxy layers

vs others: Simpler than generic Node.js caching libraries (node-cache, redis) because it's purpose-built for MCP tool semantics and requires zero changes to existing handler code

via “tool result caching and memoization”

🤗 smolagents: a barebones library for agents. Agents write python code to call tools or orchestrate other agents.

Unique: Implements transparent tool result caching with configurable backends (in-memory, Redis), allowing agents to reuse cached results and reduce redundant tool invocations without modifying agent logic.

vs others: More transparent than manual caching because it's built into the tool execution layer, but requires careful cache invalidation strategy compared to stateless function calling.

via “query result caching and optimization”

Virtual assistant that help with data analytics