Create a new intent in a Cognigy.AI flow for NLU recognition. Validate with dry run first, then train the model.
MIT
466,580 tools. Updated 2026-08-19 17:15
"How to implement intent recognition in MCP" matching MCP tools:
- Score a test utterance against trained NLU intents to evaluate recognition accuracy before deployment. Returns ranked matches with confidence scores.MIT
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- View all recognition terms configured in the TypeWhisper dictionary to review or manage custom entries.GPL 3.0
- Plan, implement, and review code changes in one call. Delegates tasks to CLI agents to automate the full development cycle.MIT
- Import a signed agent mandate and bind it to a Harbor intent with a replay-safe recognition proof. Authorizes delegated agent spend through the gateway.Apache 2.0
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- Import a signed AgentMandateV1 through gateway v2 and bind it to a Harbor intent using a replay-safe proof.Apache 2.0
- Export workspace context as CLAUDE.md, cursorrules, or intent-md files. Choose format and optionally specify an intent or product ID.MIT
- Create a structured intent spec by providing a title, objective, and product ID. Use with list_intents to avoid duplicates.MIT
- Search Clamp documentation for setup, SDK, MCP, concepts, and skills pages. Returns ranked results with URL, title, and description to help answer how-to questions.MIT
- Retrieve the server's capabilities, supported workflow patterns, validation rules, and recommended tool sequences to understand how to use this MCP server effectively.MIT
- Detect potential conflicts between your active intent and other team members' intents, highlighting overlapping files and decisions to coordinate before merging.Inno Setup
- Retrieve all decisions recorded in the current session for a given intent. Review, edit, or remove them before committing.Inno Setup
- Score a test utterance against a Cognigy.AI flow's trained NLU intents to identify the best matching intent and its confidence score. Quickly validate utterance recognition accuracy.MIT
- Update an existing intent in a Cognigy.AI flow. Validate with dryRun, then retrain the NLU model.MIT
- Learn how to use this MCP server's blockchain tools by consulting the initial guide before accessing transaction management, account insights, or token interaction features.MIT
- Get search intent classification for up to 1,000 keywords with probability scores, identifying informational, navigational, commercial, or transactional intent.Apache 2.0
- Find all contracts that implement a given function signature. Identify overrides, interface implementations, and polymorphic patterns in Solidity codebases.AGPL 3.0
- Remind developers to consult MCP tools during Aptos blockchain development to maintain proper workflow habits and implement features correctly.Apache 2.0
- Check account health, credit status, pixel counts, and intent audience availability to monitor Leadpipe MCP server performance and data resources.MIT