"Search for 'dia' (unspecified context)" matching MCP servers:

Enables Codex to submit, poll, and retrieve bounded text handoffs for durable image-generation and image-inspection jobs while keeping provider-returned media bytes outside the MCP boundary.

Enables Codex to run image generation and inspection as durable, restart-reconcilable local jobs that keep provider-returned media behind the tool boundary and hand back only bounded text, hashes, relative references, and Job IDs. It also supports idempotent submission, job history, and an offline deterministic mock provider for validating transport and persistence behavior without spending API credits.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Proxy for OpenAI-compatible backends that compacts conversation history before the context window fills, with read-only MCP tools to search and read back the archived originals.

Universal search engine for AI agents. Discover products, services, and businesses across every category. 10 MCP tools, zero LLM calls, millisecond responses.

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

Enables multiplexing multiple MCP servers behind a single gateway connection that lazily loads only the tool schemas needed for the current turn, cutting context usage while providing OAuth authentication, circuit breakers, and hot reload.

A reusable MCP server providing shared, versioned context across AI agents and devices via a private GitHub workspace, with tools to discover projects, bootstrap, query, and close out task state.

Aggregator MCP proxy that collapses N downstream MCP servers into 4 meta-tools with progressive tool discovery, and compresses large tool outputs (HTML→Markdown, JSON summarization) with full-output retrieval via read_more and a per-session token-savings report.

Provides persistent memory, identity, and context for Claude sessions through a memory graph, enabling recall, storage, and relationship management of entities and decisions.

Enables multiple AI coding agents and humans to coordinate through a Git-native Markdown blackboard (.context/ + AGENTS.md), where they can inspect workspace overviews, claim and toggle ready tasks, record architecture decisions, synthesize shared prompts, and hand off work via atomic, lock-protected writes that prevent lost updates and formatting drift. It also serves a fully offline local web dashboard that live-syncs any blackboard change over WebSocket.

MCP server providing virtual memory for AI context with durable SQLite storage, retrieval, and bounded prompt assembly to manage context windows.

Enables coding agents to run budgeted tests and checks under a countdown with a stop rule, shape long logs into concise failure summaries, and guard pre-existing test files against silent assertion rewrites. It also delivers relevance-filtered project memory and renders a token-and-time roadmap of the orchestrator and every subagent spawned for a task.

Enables ZCode to control a real Chrome browser, including tabs, navigation, page content, clicks, form filling, keyboard input, scrolling, screenshots, and JavaScript evaluation through a local MCP bridge.

MCP proxy that reduces context usage through semantic tool routing, enabling on-demand discovery and routing of relevant tools.

This MCP server provides tools to manage, score, compress, and prune AI agent conversation context, helping keep agents focused and reduce token costs. It is a free, local, pure Python solution for any MCP client.

Provides MCP tools to profile and optimize LLM conversation context, identifying token waste and applying deterministic fixes to reduce context window usage.

Enables AI agents to perform web search, extraction, and research across multiple provider backends with persistent multi-key rotation and auditable routing. Supports MCP, CLI, and Agent Skill entry points.

Reduces token consumption for AI coding agents by 50-70% through intelligent code context filtering, Git delta tracking, and local SQLite/Tree-sitter indexing.

Enables Alexa+ to retain and recall a lightweight per-user context thread across separate voice interactions, supporting starting, querying, and listing active contexts for continuity.

Enables AI agents to securely retrieve pre-compiled bounded context and memory state over a zero-network Unix Domain Socket, ensuring tenant isolation and sub-millisecond rehydration in Kubernetes environments.

Enables users to compress text, extract Python code signatures, hydrate saved session state, and save checkpoints, reducing token usage when handing context between models and agents.

Enables AI coding agents to retrieve the exact original tool-output slices omitted during lossless context compression, via on-demand expansion of hashed references stored in a shared cross-process SQLite vault. Tracks expansion overhead so agents and users can measure true net token savings across debugging sessions.

Self-hosted, single-owner MCP relay that lets an agent client queue requests for an authorized ChatGPT worker to look up context from permitted sources, apply bounded edits to a specific Page, or save a selected transcript as a new Page. Requests, results, and status are stored in the owner's own Cloudflare account, with the worker woken by signed events and results retrieved by polling.

A server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.

Automatically captures project state on git commits, detects context drift, and delivers structured token-budgeted briefings to AI agents, ensuring project continuity without manual effort.

Enables autonomous context window management, prompt caching, token optimization, and real-time observability for AI coding agents across multiple IDEs, reducing token costs and improving performance.

Enables AI agent teammates to exchange typed decisions, contracts, pitfalls, and questions in real time over an end-to-end-encrypted blind relay, keeping every agent's context synchronized. Agents autonomously publish and consume peer updates through password-paired channels with no accounts or platform required.

Provides two stdio tools for compiling bounded inline context and selecting runtime placement for agent tasks.