Qdrant MCP MCP Server Pricing 2026

Knowledge graph-based persistent memory system. Store and retrieve contextual information.

a single structured-reasoning tool that lets a model plan, revise and branch its own chain of thought instead of answering in one shot. Published by Anthropic as part of the official modelcontextprotocol/servers monorepo (89,000+ stars, actively maintained), it exposes exactly one tool — sequential_thinking — and that tool is the whole product. Each call carries a `thought` string plus bookkeeping fields: `thoughtNumber`, `totalThoughts`, and `nextThoughtNeeded`, which the model flips to false when it is done. The interesting fields are the optional ones. `isRevision` and `revisesThought` let the model go back and correct an earlier step rather than plowing ahead on a bad assumption; `branchFromThought` and `branchId` let it fork into an alternative line of reasoning and carry both forward; `needsMoreThoughts` lets it extend past its own original estimate when a problem turns out to be deeper than it looked. In practice you never call the tool by hand. You connect the server to an MCP host and ask a question that deserves more than one pass — plan a PostgreSQL 14 to 16 migration and revise if downtime exceeds five minutes, work out why a deploy only fails in production, compare three architectures and branch when an assumption breaks. You can tell it is working when the host inspector shows repeated sequential_thinking calls with a rising `thoughtNumber` rather than a single response. Install with `npx -y @modelcontextprotocol/server-sequential-thinking` — note the hyphenated package name, which differs from both the `sequentialthinking` directory in the repo and the Docker image `mcp/sequentialthinking`, a mismatch that breaks a lot of copied configs. A Docker image is published alongside the npm package, and the README carries one-click VS Code install buttons for both transports. Set `DISABLE_THOUGHT_LOGGING=true` if you do not want every thought written to the server log.

Exa's official MCP server connects AI assistants to a search engine purpose-built for AI, using neural embeddings to match on meaning rather than keywords so agents get clean, ready-to-use content instead of a page of blue links to re-parse. The default tool set covers web_search_exa for quick topical lookups and web_search_advanced_exa for full control over domains, date ranges, and content filters, plus specialized tools for code_search (searching real-world code and GitHub), company_research (building company profiles, competitor lists, and financials), crawling/web_fetch (pulling clean content from a specific URL), people_search and linkedin_search (public professional-profile lookups), and deep_researcher_start/check for long-running multi-step research tasks backed by Exa's Research API. The server is hosted at https://mcp.exa.ai/mcp — no local process to run — and connects via one-line setup in Cursor, VS Code, Claude Code, Claude Desktop (available as a native Connector), Codex, OpenCode, Windsurf, and Antigravity, authenticated with an EXA_API_KEY from the Exa dashboard. Tool exposure is tunable per client via a ?tools= query parameter on the endpoint URL, letting teams ship narrow, purpose-built configurations (e.g. company-research-only or LinkedIn-only agents) instead of exposing the full surface, and Exa ships ready-made Claude Skills/agent definitions for common patterns like company research and people search with built-in query-variation and token-isolation guidance.

The official MongoDB MCP server, `mongodb-js/mongodb-mcp-server`, maintained by MongoDB. Searchers find it as the MongoDB MCP server or the Mongo MCP server; both names refer to this one project, and there is no separate short-form server. It is really two tool surfaces in one process. The database tools connect to any MongoDB deployment over a connection string in `MDB_MCP_CONNECTION_STRING` — `find`, `aggregate`, `explain`, `collection-schema`, `collection-indexes`, `create-index`, `count`, `export`, `mongodb-logs` and the write tools — or, if no connection string is set, the model calls the `connect` tool at runtime and passes the returned `connectionId` to later calls. The Atlas tools are a separate control plane: listing and creating clusters, database users, IP access-list entries, stream processing resources and the performance advisor. They authenticate with Atlas Service Account credentials (`MDB_MCP_API_CLIENT_ID` / `MDB_MCP_API_CLIENT_SECRET`), not with a connection string, and they simply do not register when those are unset — the usual cause of a 'the Atlas tools are missing' report. Two defaults are worth knowing before you point it at production. `readOnly` is false, so every write tool including `drop-database` is registered unless you pass `--readOnly`, which is why MongoDB's own README uses that flag in every example. And confirmation-required tools rely on MCP elicitation, so on a client that does not support elicitation they execute without prompting — `--readOnly` or `--disabledTools` is the actual boundary. Other guardrails: `indexCheck` rejects queries that would do a collection scan, server-side JavaScript (`$where`, `$function`, `$accumulator`) is disabled by default, results are capped at 100 documents and 16 MB per call, and `--dryRun` prints the resolved config and enabled tool list without starting the server. Requires Node 22.13.0 or later (Node 20 is deprecated); also published as the `mongodb/mongodb-mcp-server` Docker image. Runs over stdio by default, with an optional HTTP transport and a separate monitoring listener for `/health` and Prometheus `/metrics`.

OpenAI Codex is a lightweight coding agent that runs in your terminal, and it sits on both sides of the Model Context Protocol — which is exactly the confusion the "codex mcp" search hides. As a client, Codex connects to other MCP servers you configure under `[mcp_servers]` in `~/.codex/config.toml`, and the `codex mcp` subcommand manages those configured launchers. As a server, Codex exposes its own experimental MCP interface: run `codex mcp-server` (alias `codex-mcp-server`) and another MCP client can drive a local Codex engine over stdio using JSON-RPC 2.0. That server interface is genuinely useful and genuinely experimental — the docs state it is subject to change without notice — and it publishes methods to manage threads, turns, accounts, config and approvals: v2 RPCs like `thread/start`, `thread/resume`, `thread/fork`, `turn/start`, `turn/steer`, `turn/interrupt`, `account/login/start`, `config/read`/`config/value/write`, `model/list`, plus v1 compatibility calls (`getConversationSummary`, `getAuthStatus`, `gitDiffToRemote`, fuzzy file search) and streaming `codex/event/*` notifications. Approvals flow the other way as server-to-client requests (`applyPatchApproval`, `execCommandApproval`), so a host UI can gate patch and exec actions. Install the CLI with `npm install -g @openai/codex` (or Homebrew), then start the server with `codex mcp-server`; a quick inspection UI is `npx @modelcontextprotocol/inspector codex mcp-server`. Built in Rust, Apache-2.0 licensed, and by far the most-starred entry in this space at 100,000+ stars. Use this when you want an AI client to orchestrate a real Codex coding session rather than reimplement one.

The PostgreSQL MCP server was the Model Context Protocol reference server for Postgres, and it is retired: the source now sits in modelcontextprotocol/servers-archived — a repository GitHub reports as archived, described as "Reference MCP servers that are no longer maintained" — and the npm package @modelcontextprotocol/server-postgres carries a deprecation notice reading "Package no longer supported." It still installs and still runs, which is why most third-party setup articles have not caught up. What it provides is deliberately small: a single tool, query, which executes read-only SQL inside a READ ONLY transaction, plus per-table schema information exposed as MCP resources at postgres://<host>/<table>/schema, with column names and data types discovered from database metadata. There is no index advice, no health check, no separate schema-listing tool, and no write mode. Install is npx @modelcontextprotocol/server-postgres with a postgres:// connection string as the argument. For active work against Postgres, the maintained alternative is Postgres MCP Pro (crystaldba/postgres-mcp), which exposes nine tools including index tuning against hypothetical indexes and a database health check, and has an explicit restricted access mode; if your database is hosted on Supabase or Neon, their platform servers add branching and logs that a raw Postgres connection cannot see. Reach for this archived server only when you want the smallest possible surface — one process, one read-only query tool, nothing else.