SingChat vs Cognigy AI MCP Management Server
A side-by-side look at the SingChat and Cognigy AI MCP Management Server MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
S
SingChatConversational AI · Remote HTTP/SSEFree live chat and AI support agent. Auto-create a workspace and embed from your AI editor.52GoodAuth: no auth requiredVerdict: choose SingChat for conversational ai work over a hosted endpoint; choose Cognigy AI MCP Management Server for conversational ai as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
SChoose SingChat when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a remote HTTP/SSE transport.
- Its auth model fits you: No auth required.
- You want tools like
ping, singchat_setup, get_embed_snippet.
Choose Cognigy AI MCP Management Server when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
COGNIGY_BASE_URL, COGNIGY_API_KEY, COGNIGY_DEFAULT_PROJECT_ID. - You want tools like
list_projects, list_flows, get_flow.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | S SingChat | |
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 52/100 · Good | 60/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| Auth | No auth required | API key required |
| Pricing | Free | BYOK (pay the provider) |
| Required env vars | None listed | COGNIGY_BASE_URLCOGNIGY_API_KEYCOGNIGY_DEFAULT_PROJECT_ID |
| Verified owner | Community listing | Community listing |
| GitHub stars | — | 2 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 17 | 138 |
| Full listing | View SingChat | View Cognigy AI MCP Management Server |
Tools & capabilities
SingChat tools 17
pingHealth check for the SingChat MCP server. Returns 'pong' and whether this request is authenticated to a workspace.singchat_setupGet the user up and running with SingChat live-chat. Call this FIRST. If no authenticated workspace is configured, this creates a brand-new free workspace and returns a short-lived, single-use browser claim link plus the website embed snippet. The user chooses their own sign-in details in the browser; no reusable credentials are returned to the model. If a workspace is already authenticated, it returns THIS workspace's embed snippet and dashboard link (it never creates a duplicate workspace).get_embed_snippetReturn the SingChat chat-widget embed code for this workspace's web inbox, in three flavors: plain HTML, React (useEffect), and Next.js (next/script). Requires a workspace API key. Optionally pass `platform` to get just one flavor.claim_accountReturn the link to claim (permanently secure) this SingChat workspace by setting an email and password. Use this for workspaces that were auto-created via MCP so the user does not lose access. The link is single-use and expires after 15 minutes. Requires an authenticated legacy workspace.list_conversationsList the workspace's support conversations (agent/inbox view), newest activity first. Optionally filter by status or a free-text search over the contact name/email and last message. Returns a compact list: id, contact name, status, unreadCount, last message preview, updatedAt.get_conversationFetch a single conversation with its contact details and the most recent messages (oldest→newest).reply_to_conversationSend an agent reply through a supported SingChat real-time inbox. This takes the conversation over from the AI and broadcasts the message to the visitor and agent inbox. Email, Telegram, and WhatsApp conversations are rejected before any change.resolve_conversationMark a conversation as RESOLVED (closes it). The visitor's widget will no longer show it as active.add_knowledgeTrain the AI agent by adding a knowledge source. type='url' crawls a website (up to 50 pages, fewer when the workspace knowledge-base quota is nearly full); type='qa' stores a single question/answer pair. Ingestion is asynchronous, the source starts as PENDING; poll `list_knowledge` for READY status.list_knowledgeList all knowledge documents for this workspace with their processing status (PENDING | PROCESSING | READY | FAILED) and chunk counts. Use this to check whether items added via add_knowledge have finished training.search_knowledgeSemantic search over the workspace knowledge base. Returns the most relevant chunks (with source document title and similarity score) for a query. Only searches documents that have finished training (status READY).configure_agentConfigure this workspace's AI support agent. All fields are optional, only the fields you pass are updated; the rest keep their current values. Call with no fields to just read back the current configuration. Does not touch or return any API keys.- +5 more on the listing page
Cognigy AI MCP Management Server tools 138
list_projectsLists all Cognigy.AI projects accessible by your API key. Use this to discover available projects before working with flows, intents, or other resources.list_flowsLists all flows in a Cognigy.AI project. Flows are conversation logic containers. Use this to discover flows before reading or modifying them.get_flowGets detailed metadata about a specific Cognigy.AI flow. Returns flow configuration, locale info, and timestamps. Use this to inspect a flow before modifying it.get_flow_settingsGets the settings/configuration of a Cognigy.AI flow. Returns NLU settings, thresholds, and other flow-level configurations. Use this before updating flow settings.get_latest_log_entriesGets the latest execution log entries from a Cognigy.AI project. Use this for debugging flow execution, viewing errors, or monitoring agent behavior.get_nodesLists all nodes in a Cognigy.AI flow. Nodes are the building blocks of conversation logic (Say, Question, If, Code, etc.). Use this to explore flow structure before reading specific nodes or modifying the flow.get_nodeGets detailed configuration of a specific node in a Cognigy.AI flow. Returns the node's type, label, config fields, and settings. Use this to inspect node behavior before modifying it.search_nodesSearches for nodes in a Cognigy.AI flow by text content. Finds nodes containing the search term in their configuration (messages, conditions, code, etc.). Use this to locate specific content within large flows.get_node_descriptorsGets all available node types (blueprints) that can be created in a Cognigy.AI flow. Returns node type definitions including their fields, appearance, and constraints. Use this to understand what nodes can be added to a flow.list_intentsLists all intents in a Cognigy.AI flow. Intents are the NLU triggers that match user utterances to flow logic. Use this to explore NLU configuration before training or modifying intents.get_intentGets detailed configuration of a specific intent in a Cognigy.AI flow. Returns the intent's conditions, rules, confirmation sentences, and settings. Use this to inspect NLU behavior before modifying.list_endpointsLists all endpoints in a Cognigy.AI project. Endpoints are channel connectors (Webchat, REST, Voice, etc.) that expose flows/agents to users. Use this to discover deployed channels.- +126 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
SingChat
{
"mcpServers": {
"singchat-2": {
"url": "https://mcp.singchat.org"
}
}
}
Cognigy AI MCP Management Server
{
"mcpServers": {
"tsvetang2-cognigy-ai-mcp-management-server": {
"command": "npx",
"args": [
"cognigy-ai-mcp-management-server"
],
"env": {
"COGNIGY_BASE_URL": "YOUR_COGNIGY_BASE_URL_HERE",
"COGNIGY_API_KEY": "YOUR_COGNIGY_API_KEY_HERE",
"COGNIGY_DEFAULT_PROJECT_ID": "YOUR_COGNIGY_DEFAULT_PROJECT_ID_HERE"
}
}
}
}
Frequently asked questions
SingChat is categorized under Conversational AI and uses a remote HTTP/SSE transport. Cognigy AI MCP Management Server belongs to Conversational AI and uses a local stdio subprocess. Pick SingChat for conversational ai work and Cognigy AI MCP Management Server for conversational ai.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
SingChat is listed as free. Auth: no auth required. Cognigy AI MCP Management Server is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add singchat-2 and tsvetang2-cognigy-ai-mcp-management-server as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, SingChat has a quality signal of 52/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Cognigy AI MCP Management Server has 60/100 (Good) with 3 views, 0 installs and 2 GitHub stars.