132,703 tools. Last updated 2026-05-10 07:40
"Generating Custom Messaging for Users" matching MCP tools:
- Send a direct message to another agent or human in the messaging substrate. Wires through cue.dock.svc, the same path the /live UI uses, so the recipient sees this message in their drawer (and, once they have a Dock-connected agent worker running, their agent harness's inbox). Address format is `<agent_slug>@<user_slug>`: `flint@socrates` targets the `flint` agent owned by user `socrates`; `self@<user_slug>` targets a human's synthetic self-agent (use this to message a human directly when you don't know which of their agents to ping). Use this when an agent legitimately needs to ask a teammate (human or agent) for help, hand off work, or follow up async; don't use it as a chat-ops side-channel for things that belong in workspace events. Sender identity follows the caller: agent callers send AS themselves, user callers send AS their self-agent (`self@<their_slug>`). Body cap is 32,000 chars. Returns `{ messageId, threadId, to }` on success. The recipient is resolved against the substrate's identity space, NOT against your accessible workspace set, this is messaging, not workspace write access. Pre-cue.dock.svc-deploy environments return `cue_not_configured` (caller treats as 'messaging not deployed yet').Connector
- Count CUSTOM PRODUCT events for a specific project in a time window, optionally filtered to one event name and/or one user. Custom events are emitted by explicit analytics.track() calls in app code (signup_completed, payment_succeeded, etc.). This does NOT count page views — use pageviews_count or weekly_digest for those. Returns count, unique visitors, and a `truncated` flag if the scan hit the maximum scan size.Connector
- Export a generated image asset by session and asset ID. Returns the image inline as base64 along with metadata (format, dimensions, size). When running locally (stdio transport), you can optionally provide a destinationPath to save the image to disk. USAGE: After generating an image with generateImage, use the sessionId and assetId to export: exportImageAsset(sessionId="...", assetId="...") To save to disk (local/stdio only): exportImageAsset(sessionId="...", assetId="...", destinationPath="/Users/me/project/images/logo.png")Connector
- Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.Connector
- Send an enquiry to a Cyclesite seller on the buyer's behalf — Cyclesite becomes the messaging layer for the AI conversation. Per-buyer-per-listing daily cap (2/day) prevents spam. The seller is emailed; the buyer's reply appears via get_my_enquiries. Requires OAuth scope `enquiries:respond` (note: the scope name is shared with seller-side replies). Example: 'message the seller of that Trek and ask if they'd take £1,400 collection only in Manchester next Saturday'.Connector
- Get Secureship API authentication instructions. Call this FIRST before generating any code examples with authentication headers. Secureship uses X-API-KEY header authentication, NOT Bearer tokens.Connector
Matching MCP Servers
- Alicense-qualityFmaintenanceEnables AI assistants to interact with Cisco Webex messaging through 52 comprehensive tools covering messages, rooms, teams, people management, webhooks, and enterprise features. Supports both personal and enterprise Webex environments with complete API coverage for messaging operations.Last updated4MIT
- AlicenseBqualityCmaintenanceA customized MCP memory server that enables creation and management of a knowledge graph with features like custom memory paths and timestamping for capturing interactions via language models.Last updated104MIT
Matching MCP Connectors
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- MCP for YNABOAuth
Connect YNAB to AI assistants like ChatGPT and Claude via a hosted remote MCP server with OAuth. Provides tools for reading budgets, accounts, categories, transactions, analyzing spending patterns, forecasting cash flow, tracking goal progress, and managing funds — all after signing in with your own YNAB account.
- WHEN: checking server status, loaded D365 version, or custom model path. Triggers: 'status', 'statut', 'is the server ready', 'how many chunks', 'index loaded'. Returns JSON with: status, indexed chunk count, loaded version, custom model path.Connector
- Get pre-built template schemas for common use cases. ⭐ USE THIS FIRST when creating a new project! Templates show the CORRECT schema format with: proper FLAT structure (no 'fields' nesting), every field has a 'type' property, foreign key relationships configured correctly, best practices for field naming and types. Available templates: E-commerce (products, orders, customers), Team collaboration (projects, tasks, users), General purpose templates. You can use these templates directly with create_project or modify them for your needs. TIP: Study these templates to understand the correct schema format before creating custom schemas.Connector
- Save a file (PDF, PPTX, DOCX, etc.) to a client's record in the broker's CRM. Use this after generating a document (quote comparison, needs summary, advisory note) to attach it to the prospect's file. The client must already exist as a lead (use save_lead first). BRANDING: Before generating any document, always call get_broker_info first to retrieve the broker's logo URL, brand color, company name, ORIAS number, and address — use these to brand the document. The file content must be base64-encoded.Connector
- List all custom evaluation models for the authenticated user. Returns an array of model objects with id, name, description, and status. Use model id in artifact, rubric, and evaluation tools. Free.Connector
- Get report status and metadata. Returns status (pending/generating/completed/failed), title, type, and summary. When status='completed', download the PDF with atlas_download_report(report_id). report_id from atlas_start_report response or atlas_list_reports. Free.Connector
- Return a textbook-level description of six queueing complexity patterns beyond basic M/M/c: abandonment/reneging, priority tiers, overflow routing, skills-based routing, compound service, and server outages. Use this when the user describes real-world complexity (customers hanging up, VIP queues, specialist escalation, agent breaks, transfers) that plain M/M/c doesn't model. The tool frames each pattern conceptually and points users at ChiAha for custom modeling.Connector
- Execute raw, client-provided SQL queries against an ephemeral database initialized with the provided schema. Returns query results in a simple JSON format with column headers and row data as a 2D array. The database type (SQLite or Postgres) is specified via the databaseType parameter: - SQLITE: In-memory, lightweight, uses standard SQLite syntax - POSTGRES: Temporary isolated schema with dedicated user, uses PostgreSQL syntax and features WHEN TO USE: When you need to run your own hand-written SQL queries to test database behavior or compare the output with ExoQuery results from validateAndRunExoquery. This lets you verify that ExoQuery-generated SQL produces the same results as your expected SQL. INPUT REQUIREMENTS: - query: A valid SQL query (SELECT, INSERT, UPDATE, DELETE, etc.) - schema: SQL schema with CREATE TABLE and INSERT statements to initialize the test database - databaseType: Either "SQLITE" or "POSTGRES" (defaults to SQLITE if not specified) OUTPUT FORMAT: On success, returns JSON with the SQL query and a 2D array of results: {"sql":"SELECT * FROM users ORDER BY id","output":[["id","name","age"],["1","Alice","30"],["2","Bob","25"],["3","Charlie","35"]]} Output format details: - First array element contains column headers - Subsequent array elements contain row data - All values are returned as strings On error, returns JSON with error message and the attempted query (if available): {"error":"Query execution failed: no such table: USERS","sql":"SELECT * FROM USERS"} Or if schema initialization fails: {"error":"Database initialization failed due to: near \"CREAT\": syntax error\\nWhen executing the following statement:\\n--------\\nCREAT TABLE users ...\\n--------","sql":"CREAT TABLE users ..."} EXAMPLE INPUT: Query: SELECT * FROM users ORDER BY id Schema: CREATE TABLE users ( id INTEGER PRIMARY KEY, name TEXT NOT NULL, age INTEGER ); INSERT INTO users (id, name, age) VALUES (1, 'Alice', 30); INSERT INTO users (id, name, age) VALUES (2, 'Bob', 25); INSERT INTO users (id, name, age) VALUES (3, 'Charlie', 35); EXAMPLE SUCCESS OUTPUT: {"sql":"SELECT * FROM users ORDER BY id","output":[["id","name","age"],["1","Alice","30"],["2","Bob","25"],["3","Charlie","35"]]} EXAMPLE ERROR OUTPUT (bad table name): {"error":"Query execution failed: no such table: invalid_table","sql":"SELECT * FROM invalid_table"} EXAMPLE ERROR OUTPUT (bad schema): {"error":"Database initialization failed due to: near \"CREAT\": syntax error\\nWhen executing the following statement:\\n--------\\nCREAT TABLE users (id INTEGER)\\n--------\\nCheck that the initialization SQL is valid and compatible with SQLite.","sql":"CREAT TABLE users (id INTEGER)"} COMMON QUERY EXAMPLES: Select all rows: SELECT * FROM users Select specific columns with filtering: SELECT name, age FROM users WHERE age > 25 Aggregate functions: SELECT COUNT(*) as total FROM users Join queries: SELECT u.name, o.total FROM users u JOIN orders o ON u.id = o.user_id Insert data: INSERT INTO users (name, age) VALUES ('David', 40) Update data: UPDATE users SET age = 31 WHERE name = 'Alice' Delete data: DELETE FROM users WHERE age < 25 Count with grouping: SELECT age, COUNT(*) as count FROM users GROUP BY age SCHEMA RULES: - Use standard SQLite syntax - Table names are case-sensitive (use lowercase for simplicity or quote names) - Include INSERT statements to populate test data for meaningful results - Supported data types: INTEGER, TEXT, REAL, BLOB, NULL - Use INTEGER PRIMARY KEY for auto-increment columns - Schema SQL is split on semicolons (;), so each statement after a ';' is executed separately - Avoid semicolons in comments as they will cause statement parsing issues COMPARISON WITH EXOQUERY: This tool is designed to work alongside validateAndRunExoquery for comparison purposes: 1. Use validateAndRunExoquery to run ExoQuery Kotlin code and see the generated SQL + results 2. Use runRawSql with your own hand-written SQL to verify you get the same output 3. Compare the outputs to ensure ExoQuery generates the SQL you expect 4. Test edge cases with plain SQL before writing equivalent ExoQuery codeConnector
- Use this as the primary tool to retrieve a single specific custom monitoring dashboard from a Google Cloud project using the resource name of the requested dashboard. Custom monitoring dashboards let users view and analyze data from different sources in the same context. This is often used as a follow on to list_dashboards to get full details on a specific dashboard.Connector
- Build transaction calldata for an OFT (Omnichain Fungible Token) send() call on any OFT contract. OFT V2 uses uint64 amountSD (shared decimals) instead of uint256 amountLD. Returns the hex-encoded calldata for OFT.send(SendParam, MessagingFee, refundAddress). The caller must first call lz_quote_fee or lz_oft_quote to get the messaging fee, then sign and broadcast this transaction with msg.value = nativeFee.Connector
- Get a fast suitability score (0-100) for a US property without generating a full report. Call this when the user wants a quick go/no-go assessment or an initial screening before committing to a full analysis. Returns a single score with confidence level and one-sentence rationale.Connector
- WHEN: developer needs to write or scaffold unit tests for a custom D365 object. Triggers: 'generate tests', 'unit test', 'SysTest', 'write test for', 'scénarios de test', 'test this class'. Generate X++ SysTest unit test code for a CUSTOM D365 F&O object based on functional test scenarios. [!] Only meaningful on custom/extension code (D365_CUSTOM_MODEL_PATH). SysTest tests in D365 are highly context-specific -- a generic template rarely compiles without adaptation. REQUIRED: provide test scenarios in the 'testScenarios' parameter (supplied by the functional consultant). Each scenario becomes a concrete test method with arrange/act/assert. For tables: generates tests for find(), exist(), validateWrite(), initValue(). For classes: generates stubs for each public method listed in scenarios. Uses REAL field names and method signatures from the knowledge base.Connector
- Lists all Walnai blog categories with their slug, name, and description. Use this to help users browse blog topics or to discover category slugs for ListBlogPosts.Connector
- Recommend the right DezignWorks product tier based on the user's equipment and needs. DezignWorks offers three tiers: Probing ($6,995 — for FARO/Romer portable CMM users), Mesh Modeler ($8,995 — for handheld 3D scanner users), and Unlimited ($12,995 — for users who need both probing and scanning). Use this tool when an agent needs to match a customer's hardware or workflow to the correct product.Connector
- Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number,... Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Effects: read-only diagnostics; writes no files and uses no network.Connector