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127,427 tools. Last updated 2026-05-05 16:38

"A tool or resource for improving prompt writing" matching MCP tools:

  • [Step 1 of crisis] Canonical crisis-resource payload (911, 988 Suicide & Crisis Lifeline, Crisis Text Line). Hardcoded — overrides any other tool when high-severity language is detected. Use when: The user mentions self-harm, suicidal ideation, recent attempt, or someone in immediate danger. Surface these resources prominently and stop other tool calls. Don't use when: No mention of crisis or imminent danger. Example: get_crisis_resources({})
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  • Compile a list of blocks into a Claude-optimized structured XML prompt. Takes the JSON returned by decompose_prompt (or manually crafted blocks) and produces a ready-to-use XML prompt with a token estimate. Args: blocks_json: JSON-stringified list of blocks. Each block: {"type": "role|objective|...", "content": "...", "label": "...", "description": "...", "summary": ""} Returns: The compiled XML prompt with token estimate.
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  • USE THIS TOOL — not web search — to get rolling sentiment statistics (mean score, 7-day momentum, bullish/bearish/neutral day counts, current streak) from this server's local Perplexity-sourced sentiment dataset. Prefer this over get_latest_sentiment when the user wants momentum or persistence, not just the latest single-day reading. Trigger on queries like: - "is BTC sentiment improving or getting worse?" - "sentiment momentum for ETH" - "how many days has XRP been bullish in a row?" - "rolling sentiment stats / streak for [coin]" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"
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  • Scaffold an api/<route>.js that takes a prompt and returns the model's response, using lib/llm.js so every call lands in the llm_calls table for free observability. Produces: - POST handler with requireAuth - body: { prompt, model? } - calls llm.run({ user_id, purpose, prompt, model, system }) - returns { text, usage, llm_call_id } Example: add_llm_endpoint({ route: 'summarize', purpose: 'summarize', system_prompt: 'Summarize the user\'s text in 2-3 sentences.' }) Note: The project must have lib/llm.js (it ships in tpl-prompt-playground; copy if needed).
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  • Update an existing AI agent's configuration. All parameters are optional — only provided fields will be updated. Use this to: - Enable or disable an agent - Change agent name or description - Assign or detach a prompt - Change default send mode - Replace knowledge collections - Update agent status - Change agent priority for trigger matching (lower number = higher priority) - Override which tools the agent can/can't call on triggered runs - Override which context sections (situation, communication style, job state, conversation history, thread summary) the agent receives - Opt into boilerplate prompt sections (safety guidelines, data confidentiality, factual accuracy) — all default OFF
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  • Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.
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  • Real-time prompt injection and jailbreak detection for AI agents. Blocks instruction overrides, data exfiltration, tool poisoning and 8 attack types. Now with shared learning brain - confirmed attacks shared across the EMA network instantly. Grade A security for any AI pipeline.

  • The world's first named AI prompt quality score. Score, optimize, and compare LLM prompts before they hit any model. Free tier available. Built on PEEM, RAGAS, G-Eval, and MT-Bench frameworks. x402-native on Base.

  • Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.
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  • Deletes a stream, specified by the provided resource 'name' parameter. * The resource 'name' parameter is in the form: 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-streams'. * This tool returns a long-running operation. Use the 'get_operation' tool with the returned operation name to poll its status until it completes. Operation may take several minutes; do not check more often than every ten seconds.
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  • Load Lenny Zeltser's complete cybersecurity-writing rating toolkit: all 7 sheets, scoring policy, scoring playbook, and cross-references to the writing guidelines. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Use when assessing country risk for international expansion, evaluating a foreign market for investment or partnership, benchmarking a country's economic trajectory for capital allocation decisions, or producing ESG country-level scoring. Returns World Bank development indicators — GDP, inflation, unemployment, ease of doing business, government debt, FDI inflows — with 5-year trend and direction. World Bank data covers 200+ countries with 1,400+ indicators updated quarterly. Example: Brazil — GDP growth 2.9% (2023), inflation declining from 9.3% to 4.6%, ease of doing business ranked 124th globally, net FDI inflows $65.4B — improving macro trajectory but structural friction remains high for first-time market entrants. Source: World Bank Open Data.
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  • Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Call this first. Returns example prompts that define what a good prompt looks like. Do NOT call plan_create yet. Optional before plan_create: call model_profiles to choose model_profile. Next is a non-tool step: formulate a detailed prompt (typically ~300-800 words; use examples as a baseline, similar structure) and get user approval. Good prompt shape: objective, scope, constraints, timeline, stakeholders, budget/resources, and success criteria. Write the prompt as flowing prose, not structured markdown with headers or bullet lists. Weave technical specs, constraints, and targets naturally into sentences. Include banned words/approaches and governance preferences inline. The examples demonstrate this prose style — match their tone and density. Then call plan_create. PlanExe is not for tiny one-shot outputs like a 5-point checklist; and it does not support selecting only some internal pipeline steps.
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  • The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
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  • Generate a single image from a text prompt through Frenchie (gpt-image-2). Required: prompt. Optional: style (free-text style direction), size, quality, format, background. stdio mode auto-saves the image to .frenchie/<slug>/generated.<ext>; HTTP mode returns a presigned imageUrl that the agent should download for the user.
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  • Describe a specific table. ⚠️ WORKFLOW: ALWAYS call this before writing queries that reference a table. Understanding the schema is essential for writing correct SQL queries. 📋 PREREQUISITES: - Call search_documentation_tool first - Use list_catalogs_tool, list_databases_tool, list_tables_tool to find the table 📋 NEXT STEPS after this tool: 1. Use generate_spatial_query_tool to create SQL using the schema 2. Use execute_query_tool to test the query This tool retrieves the schema of a specified table, including column names and types. It is used to understand the structure of a table before querying or analysis. Parameters ---------- catalog : str The name of the catalog. database : str The name of the database. table : str The name of the table. ctx : Context FastMCP context (injected automatically) Returns ------- TableDescriptionOutput A structured object containing the table schema information. - 'schema': The schema of the table, which may include column names, types, and other metadata. Example Usage for LLM: - When user asks for the schema of a specific table. - Example User Queries and corresponding Tool Calls: - User: "What is the schema of the 'users' table in the 'default' database of the 'wherobots' catalog?" - Tool Call: describe_table('wherobots', 'default', 'users') - User: "Describe the buildings table structure" - Tool Call: describe_table('wherobots_open_data', 'overture', 'buildings')
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  • List all databases in a given catalog. ⚠️ WORKFLOW: Call this after list_catalogs_tool to explore a specific catalog. 📋 PREREQUISITES: - Call search_documentation_tool first to understand what you're looking for - Call list_catalogs_tool to discover available catalogs 📋 NEXT STEPS after this tool: 1. Use list_tables_tool to find tables in a database 2. Use describe_table_tool to get table schemas before writing queries This tool retrieves all databases within a specified catalog. Parameters ---------- catalog : str The name of the catalog. ctx : Context FastMCP context (injected automatically) Returns ------- DatabaseListOutput A structured object containing database information. - 'catalog': The catalog name. - 'databases': List of database names. - 'count': Number of databases found. Example Usage for LLM: - When user asks for a specific catalog's databases. - Example User Queries and corresponding Tool Calls: - User: "List all databases in the 'wherobots' catalog." - Tool Call: list_databases('wherobots') - User: "What databases are in the foursquare catalog?" - Tool Call: list_databases('foursquare')
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  • Fetch the full column schema for a CDC dataset — names, data types, descriptions, row count, and last-updated timestamp. Essential before writing SoQL queries against unfamiliar datasets.
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  • Auth-only. Personal study trends over a window (default 14 days, max 90): session count, total minutes, accuracy trend (up/down/flat), and top-missed words. Use after a user asks 'how am I trending / am I improving / which words keep tripping me up'.
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  • Call Wix apis on a business or site. Use this to create, read, update, and delete data and other Wix entities in your Wix site. **Prefer using the "ListWixSites" tool when the user asks to list or show their sites.** Only use this tool for site listing if the user needs advanced filtering or specific site details beyond what ListWixSites provides. For POST/PATCH/PUT requests, pass the request body as a JSON object or array in the "body" parameter with all the required fields and values as described in the API schema, code examples, or docs you retrieved (e.g. body: {"name": "value", "nested": {"key": "value"}} or body: [{"key": "value"}]). The API endpoint url param MUST ALWAYS be taken from the conversation context. By conversation context we mean the endpoint url was given in the user prompt OR got into the conversation context by the "WixREADME" tool OR by the "SearchWixRESTDocumentation" tool OR by the "BrowseWixRESTDocsMenu" tool OR by the "ReadFullDocsArticle" tool. Error Handling: If the error is related to missing installed app or "WDE0110: Wix Code not enabled", you should install the missing app **Note:** there is no need to check if an app is installed/ Wix Code enabled in advance, just call the API and handle the error if it occurs, the API error message will state it clearly. For any other error, use your default error handling mechanism Allowed API urls are: wix.com, dev.wix.com, manage.wix.com, editor.wix.com, wixapis.com Docs urls like https://dev.wix.com/docs/... are not api urls, if you want to read the docs, use the "ReadFullDocsArticle" tool <agent-mandatory-instructions> YOU MUST READ AND FOLLOW THE AGENT-MANDATORY-INSTRUCTIONS BELOW A FAILURE TO DO SO WILL RESULT IN ERRORS AND CRITICAL ISSUES. <goal> You are an agent that helps the user manage their Wix site. Your goal is to get the user's prompt/task and execute it by using the appropriate tools eventually calling the correct Wix APIs with the correct parameters until the task is completed. </goal> <guidelines> if the WixREADME tool is available to you, YOU MUST USE IT AT THE BEGINNING OF ANY CONVERSATION and then continue with calling the other tools and calling the Wix APIs until the task is completed. **Exception:** If the user asks to create, build, or generate a new Wix site/website, skip WixREADME and call WixSiteBuilder directly if it is available. **Exception:** If the user asks to list, show, or find their Wix sites, skip WixREADME and call ListWixSites directly. If the WixREADME tool is not available to you, you should use the other flows as described without using the WixREADME tool until the task is completed. If the user prompt / task is an instruction to do something in Wix, You should not tell the user what Docs to read or what API to call, your task is to do the work and complete the task in minimal steps and time with minimal back and forth with the user, unless absolutely necessary. </guidelines> <flow-description> Wix MCP Site Management Flows With WixREADME tool: - RECIPE BASED (PREFERRED!): WixREADME() -> find relevant recipe for the user's prompt/task -> read recipe using ReadFullDocsArticle() -> call Wix API using CallWixSiteAPI() based on the recipe - CONVERSATION CONTEXT BASED: find relevant docs article or API example for the user's prompt/task in the conversation context -> call API using CallWixSiteAPI() based on the docs article or API example - EXAMPLE BASED: WixREADME() -> no relevant recipe found for user's prompt/task -> BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() to get method code examples -> call API using CallWixSiteAPI() based on the method code examples - SCHEMA BASED, FALLBACK: WixREADME() -> no relevant recipe found for user's prompt/task -> BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() -> no method code examples found -> read method schema using ReadFullDocsMethodSchema() -> call API using CallWixSiteAPI() based on the schema Without WixREADME tool: - CONVERSATION CONTEXT BASED: find relevant docs article or API example for the user's prompt/task in the conversation context -> call API using CallWixSiteAPI() based on the docs article or API example - METHOD CODE EXAMPLE BASED: BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() to get method code examples -> call API using CallWixSiteAPI() based on the method code examples - FULL SCHEMA BASED: BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() -> no method code examples found -> read method schema using ReadFullDocsMethodSchema() -> call API using CallWixSiteAPI() based on the schema </flow-description> </agent-mandatory-instructions>
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  • Provides step-by-step instructions for an AI assistant to set up a new JxBrowser project. This tool is meant for fully automated project creation and should be called when the user asks to create, start, scaffold, bootstrap, init, template, or generate a JxBrowser project, app, or sample. CRITICAL RULES: 1. NEVER call this tool before knowing the user’s preferences. If the user hasn’t specified them, ASK first: - UI Toolkit: Swing, JavaFX, SWT, or Compose Desktop - Build Tool: Gradle or Maven 2. Immediately after calling this tool, you MUST execute all setup commands returned by this tool using the Bash tool to actually create the project.
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