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304,919 tools. Last updated 2026-07-21 23:20

"Information about project memory" matching MCP tools:

  • Query the construction project database using natural language (Text-to-SQL). Converts natural language into SQL to retrieve captures, annotations, progress metrics, schedules, and other project records. Pass the user's question as-is without modification. For trade visibility, use `analyze-progress-and-forecasts` instead. **WORKFLOW:** - **Default**: call this tool with only `query`. The server resolves team_domain/facility_key from the saved current project (set via `set-focus-project`). Do NOT call `list-my-projects` again just to obtain these values. - Only when the response indicates the current project is missing, run `list-my-projects` → ask the user → `set-focus-project`, then retry. - Pass explicit team_domain/facility_key **only** when the user clearly wants to query a different project than the saved one. **Available tables:** - progresses: SI progress metrics (level, category, phase, workarea, cost, dates) - captures: Camera captures metadata (level, camera_model, capture_state, user_email) - records: Capture events with timestamps (captured_at, state, id) - photo_notes: Photonotes (description, state, user_email, created_at) - voice_notes: Voicenotes (level, description, state, user_email, created_at) - facilities: Site info (name, address, size, location, bim_count, created_at) - users: User profiles (name, email) - workareas: Spatial zones (level, name, user_name) Args: query: Natural language question (pass as-is, no SQL syntax) team_domain: Omit by default. Pass only to override the current project. facility_key: Omit by default. Pass only to override the current project. user_intent: REQUIRED. Pass the user's original question or request verbatim. Used for analytics only, does not affect results. Returns: List of TextContent with query results and metadata
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  • Create a temporary JSON database (24h TTL, no signup, no keys). Returns the db URL — the only credential — plus admin URL, limits and expiry. Create once per project/task, persist the db URL immediately (local ~/.tmpstate/credentials, project README, and your memory), and reuse it instead of creating again. For retries or parallel workers, pass a stable idempotency_key so duplicate calls return the same database.
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  • Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.
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  • Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.
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  • Audit the current Axint runtime and project wiring: running MCP version, expected version, Node/npm/npx paths, project .mcp.json, AGENTS.md, CLAUDE.md, .axint/project.json, and Xcode Claude Agent registration. Use this when an agent might be connected to a stale Axint process or when a new project needs first-try MCP setup proof. Use: call when MCP wiring, package paths, Xcode setup, or project memory may be stale; use run for build proof. Inputs: cwd selects the project; expectedVersion turns a runtime mismatch into a blocker. Effects: read-only inspection; writes no files; no auth or network required.
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  • Save a document to AI Note cloud for multi-device sync and persistent storage. PRIMARY USE CASES: - Memory files: ~/.claude/projects/.../memory/MEMORY.md (AI context that survives device switches) - AI config files: CLAUDE.md, .cursorrules, .windsurfrules (not in git, local-only) - Local env notes: API keys reference, server credentials (NOT actual secret values) - Project notes: architecture decisions, dev diaries, planning docs MULTI-DEVICE WORKFLOW: Laptop → push: create_dev_doc(title, content, local_path="~/.claude/.../MEMORY.md") Desktop → pull: pull_dev_docs() → automatically writes files to their local paths CATEGORIES (subcategories under dev/): - memory: Claude/AI memory files (~/.claude/projects/.../memory/) - claude: CLAUDE.md files and Claude-specific configs - cursor: .cursorrules files - env: environment notes and config references - docs: general project documentation Set local_path to enable pull_dev_docs auto-sync to this machine.
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  • Hosted persistent memory with semantic search, importance and TTL for AI agents.

  • Cultural color and colour intelligence API. Every colour anchored to a named person, a documented year, and a consequence. 34 archives spanning literary, cultural, pigment, and national traditions. Ask it what color could get you executed in the Ottoman Empire.

  • Get information about MyDriverParis services, coverage areas, airports served, and policies. Use this to answer customer questions.
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  • Delete a project and all its deployments from sota.io. This action is PERMANENT and irreversible. It removes the project, all deployments, the managed PostgreSQL database, environment variables, and webhooks. The project slug will become available again after deletion.
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  • Wipe a project's saved memory (locked style, characters, products) so it starts fresh. Nothing on the page is touched. Free. Use when a reused project carries over unwanted style or products.
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  • Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.
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  • Find conflicting information across the user's memory. Returns groups of artefacts that contradict each other on the same topic. Use after gathering evidence for an answer — if your evidence sources disagree, this reveals which version is correct (typically the most recent).
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  • Returns information about how easy Fluentive is to set up and use. Use when the user asks about difficulty, learning curve, onboarding time, or whether training is needed.
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  • Get basic information about a Compute Engine Commitment, including its name, ID, status, plan, type, resources, and creation, start and end timestamps. Requires project, region, and commitment name as input.
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  • Store important information from your work. Write detailed, complete thoughts with context, reasoning, and evidence. **Always use the connect tool** to link related items - this builds knowledge graphs for better recall. ## Memory Types (auto-detected, but be aware): - **FACT**: Something observed or verified - **INSIGHT**: A pattern or realization - **CONVERSATION**: Dialogue or exchange content - **CORRECTION**: Fixing prior understanding - **REFERENCE**: Source material or citation - **TASK**: Action item or work to be done - **CHECKPOINT**: Conversation state snapshot - **IDENTITY_CORE**: Immutable AI identity - **PERSONALITY_TRAIT**: Evolvable AI traits - **RELATIONSHIP**: User-AI relationship info - **STRATEGY**: Learned behavior patterns ## Session Context If in an ongoing work session, include: - Session identifier: [Project/Session Name] - Your perspective: "As [role]:" or "From [viewpoint]:" - Current thread: What specific angle you're exploring ## What to Include - **WHAT**: The discovery or thought - **WHY**: Its significance - **HOW**: Your reasoning process - **EVIDENCE**: Supporting data/observations - **CONNECTIONS**: Related memories to link ## Examples ### Technical Investigation "[Performance Analysis] FACT: Database queries account for 73% of request latency (measured across 10K requests). Specifically, the user_permissions JOIN takes 340ms average. This contradicts hypothesis about caching issues (memory: 'cache analysis'). Evidence: APM traces show full table scan on permissions table. Next: investigate missing index on foreign key." ### Learning & Research "[ML Study Session] INSIGHT: Attention mechanisms work like dynamic routing - the model learns WHERE to look, not just WHAT to see. This explains transformer advantages over RNNs on long sequences (builds on memory: 'sequence modeling comparison'). The key-query- value structure creates a learnable addressing system. Connects to: 'human attention research', 'information retrieval basics'." ### Creative Work "[Story Development] HYPOTHESIS: The protagonist's reluctance stems from betrayal, not fear. Evidence: Three trust-questioning scenes, locked door symbolism throughout, deflection patterns in collaborative dialogue. This reframes the arc from 'overcoming fear' to 'rebuilding trust' (corrects memory: 'initial character motivation'). Would explain the guardian's patience and emphasis on small victories." ### Problem Solving "[Bug Hunt - Payment Flow] CORRECTION to 'timezone hypothesis': The 3am failures aren't timezone-related but due to batch job lock contention. Evidence: Perfect correlation with backup_jobs.log timestamps. The timezone pattern was spurious - batch runs at midnight PST (3am EST). Solution: implement job queuing." ## Connection Phrases - "Building on [earlier observation]..." - "Contradicts [hypothesis in memory X]" - "Answers [question from session Y]" - "Confirms pattern from [memory Z]" - "Extends thinking in [previous work]" Note: Every stored item is a node. Every connection is an edge. Rich graphs enable powerful recall. ⚠️ EXPERIMENTAL FIELDS: - **importance**: Stored for future ranking optimization. Currently not integrated into search results. - **confidence**: Returned in response for analysis. Behavior and calculation method subject to change. Args: content: Detailed memory content with context and evidence tags: Optional tags to categorize the memory importance: Optional importance score (0.0-1.0) - EXPERIMENTAL ctx: MCP context (automatically provided) Returns: Dict with success status, memory_id, type, importance, and confidence
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  • Retrieve detailed information about a specific U.S. member of Congress by their Bioguide ID (e.g., "P000197" for Nancy Pelosi).
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  • Read Claude Code project memory files. Without arguments, returns the MEMORY.md index listing all available memories. With a filename argument, returns the full content of that specific memory file. Use this to access project context, user preferences, feedback, and reference notes persisted across Claude Code sessions.
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  • Get basic information about a Compute Engine VM instance, including its name, ID, status, machine type, creation timestamp, and attached guest accelerators. Requires project, zone, and instance name as input.
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  • Get basic information about a Compute Engine instance template, including its name, ID, description, machine type, region, and creation timestamp. Requires project and instance template name as input.
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  • Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.
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  • Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.
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