ChipsAI MCP Server vs Cognigy AI MCP Management Server
A side-by-side look at the ChipsAI MCP Server 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.
Verdict: choose ChipsAI MCP Server for conversational ai work as a local process; 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?
Choose ChipsAI MCP Server when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
CHIPSAI_API_KEY, CHIPSAI_API_URL, CHIPSAI_USERNAME, CHIPSAI_PASSWORD. - You want tools like
list_chatbots, get_chatbot, create_chatbot.
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 | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 47/100 · Fair | 60/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | BYOK (pay the provider) |
| Required env vars | CHIPSAI_API_KEYCHIPSAI_API_URLCHIPSAI_USERNAMECHIPSAI_PASSWORD | COGNIGY_BASE_URLCOGNIGY_API_KEYCOGNIGY_DEFAULT_PROJECT_ID |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 2 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 25 | 138 |
| Full listing | View ChipsAI MCP Server | View Cognigy AI MCP Management Server |
Tools & capabilities
ChipsAI MCP Server tools 25
list_chatbotsList all chatbots for the authenticated userget_chatbotGet full chatbot details (prompt, model, colors, etc.)create_chatbotCreate a new chatbot (returns embed script tag)update_chatbotUpdate chatbot fields (name, prompt, model, theme, colors, etc.)delete_chatbotSoft-delete (deactivate) a chatbotget_chatbot_configGet public widget configurationget_chatbot_analyticsGet analytics: messages, sessions, daily stats, devices, countriesupload_documentUpload PDF/DOC/DOCX to a chatbot's knowledge base (LlamaParse)list_conversationsList conversations, optionally filtered by chatbotcreate_conversationCreate a new conversationget_conversationGet conversation detailsupdate_conversationUpdate conversation title- +13 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.
ChipsAI MCP Server
{
"mcpServers": {
"chipsai-mcp-server": {
"command": "uvx",
"args": [
"chipsai-mcp"
],
"env": {
"CHIPSAI_API_KEY": "YOUR_CHIPSAI_API_KEY_HERE",
"CHIPSAI_API_URL": "YOUR_CHIPSAI_API_URL_HERE",
"CHIPSAI_USERNAME": "YOUR_CHIPSAI_USERNAME_HERE",
"CHIPSAI_PASSWORD": "YOUR_CHIPSAI_PASSWORD_HERE"
}
}
}
}
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
ChipsAI MCP Server is categorized under Conversational AI and uses a local stdio subprocess. Cognigy AI MCP Management Server belongs to Conversational AI and uses a local stdio subprocess. Pick ChipsAI MCP Server 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.
ChipsAI MCP Server has no declared pricing — check the project's own site. 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 chipsai-mcp-server 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, ChipsAI MCP Server has a quality signal of 47/100 (Fair) 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.