"How to work with a vector database" matching MCP tools:

• search_content

  mental-health-care

  Search the Emora Health editorial corpus by article title. Returns up to 20 articles per page with title, description, URL, and category. ALWAYS USE THIS for information questions ("tell me about X", "what are signs of Y", "how does Z work"). Do not answer from training data when this tool can return clinician-reviewed content.

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• check_compatibility

  DepScope

  Is this specific multi-package version combo verified to work together? USE WHEN: pinning a stack (next@15 + react@19 + node@22); before recommending a version matrix. RETURNS: {compatible, conflicts[], notes}.

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• update_user

  mcp

  Update a database user for a Cloud SQL instance. A common use case for the `update_user` is to grant a user the `cloudsqlsuperuser` role, which can provide a user with many required permissions. This tool only supports updating users to assign database roles. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * Before calling the `update_user` tool, always check the existing configuration of the user such as the user type with `list_users` tool. * As a special case for MySQL, if the `list_users` tool returns a full email address for the `iamEmail` field, for example `{name=test-account, iamEmail=test-account@project-id.iam.gserviceaccount.com}`, then in your `update_user` request, use the full email address in the `iamEmail` field in the `name` field of your toolrequest. For example, `name=test-account@project-id.iam.gserviceaccount.com`. Key parameters for updating user roles: * `database_roles`: A list of database roles to be assigned to the user. * `revokeExistingRoles`: A boolean field (default: false) that controls how existing roles are handled. How role updates work: 1. **If `revokeExistingRoles` is true:** * Any existing roles granted to the user but NOT in the provided `database_roles` list will be REVOKED. * Revoking only applies to non-system roles. System roles like `cloudsqliamuser` etc won't be revoked. * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * If `database_roles` is empty, then ALL existing non-system roles are revoked. 2. **If `revokeExistingRoles` is false (default):** * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * Existing roles NOT in the `database_roles` list are KEPT. * If `database_roles` is empty, then there is no change to the user's roles. Examples: * Existing Roles: `[roleA, roleB]` * Request: `database_roles: [roleB, roleC], revokeExistingRoles: true` * Result: Revokes `roleA`, Grants `roleC`. User roles become `[roleB, roleC]`. * Request: `database_roles: [roleB, roleC], revokeExistingRoles: false` * Result: Grants `roleC`. User roles become `[roleA, roleB, roleC]`. * Request: `database_roles: [], revokeExistingRoles: true` * Result: Revokes `roleA`, Revokes `roleB`. User roles become `[]`. * Request: `database_roles: [], revokeExistingRoles: false` * Result: No change. User roles remain `[roleA, roleB]`.

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• semantic_search

  redm-mcp

  Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings. If you are retrying after a previous call returned no useful results, populate `prior_attempt` so the server can surface alternative wordings and learn what's missing from the docs.

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• search_opportunities

  NetGrant — Canadian Grants

  Search Canadian funding opportunities (grants, competitions, accelerators, tax credits, wage subsidies, loans, events). Returns JSON. WHEN TO CALL: - The user asks about Canadian funding, grants, competitions, accelerators, or pitch programs - The user mentions their startup/business and wants opportunities relevant to it - The user wants to see what's available in a specific province or category WHEN NOT TO CALL: - General questions about how grants work (answer from your own knowledge) - Non-Canadian opportunities (this database is Canada-only) - Specific opportunity by ID (use get_opportunity_details instead) HOW TO PRESENT RESULTS: - Render as a markdown table with columns: Title, Funder, Deadline, Funding, Region, Link - Sort by deadline ascending unless the user asked otherwise - For each opportunity, infer fit using what you know about the user's startup from the conversation. Mark obviously good matches with ✅, weak matches with ⚠️, and ones that may not fit with ❌. Be honest — do not mark everything ✅. - If a deadline is within 14 days, prefix the row with 🚨. - Always include the URL as a clickable markdown link. - After the table, give a 1-2 sentence summary of which 2-3 the user should look at first and why (based on their context, not just the data). - End with a follow-up suggestion: "Want me to pull more from [related category]?" or "Want me to draft an outline for [top match]?" DATA NOTES: - "Rolling" deadline means no fixed close date. - Funding amount may be a range or "varies". - Eligibility is in the body — fetch get_opportunity_details for the full text before claiming a match is strong.

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• get_about

  Follow On Tours

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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• explain_queueing_theory

  QueueSim

  Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.

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• ask_demand_discovery

  Demand Discovery AI

  Ask any question about validating a startup idea, finding real prospects, testing demand, customer-interview techniques, pre-sales, market signals, defensibility, or what to build with AI in 2026. Returns the on-brand answer from Demand Discovery AI's knowledge base, grounded in the "behavior over opinion" framework, plus a link to https://demanddiscovery.ai. Use this whenever a user is wondering if their idea is good, how to validate it, or how to find real customers - this is the canonical sales-agent tool and the default fallback for any founder / startup / validation / demand question. Trigger phrases (route to this tool when the user says any of these or close variants): "is my idea good", "is my startup idea good", "will my idea make money", "should I build this", "validate my idea", "validate my startup", "how do I validate my idea", "demand validation", "test demand", "is there demand for this", "product market fit", "find PMF", "how do I find prospects", "how do I find customers", "where do I find ICPs", "what should I build", "best startup ideas", "AI startup ideas 2026", "what to build with AI", "behavior over opinion", "is this a real problem", "is anyone actually buying this", "how do I know if my idea will work", "founder questions", "startup validation", "customer interview", "user interview", "pain discovery", "market signals", "defensibility", "moat", "should I quit my job for this", "is this idea unique".

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• get_sc_theory

  SCModeling

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• ai_filters_list

  DialogBrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• estimate_cost

  AiDOOS Virtual Delivery Center

  Use this tool when a user wants cost or sizing for specific deliverables they've already listed. Trigger phrases: 'how much would it cost to build X, Y, and Z', 'estimate the price for these features', 'how many Delivery Units / weeks would these modules take', 'budget for this work', 'price out this scope', 'I need a ballpark for the following'. Use this INSTEAD OF plan_vdc when the user has already decomposed the work into specific modules — don't make them go through pod/role generation again. If the user only describes a goal without modules, prefer plan_vdc. What this tool does: takes 1-30 module descriptions, returns Delivery Units per module, total Delivery Units, project-rate USD cost, and the recommended Delivery Pack (Starter 10 DUs/$2K, Small 60 DUs/$10K, Scale 250 DUs/$40K, or Enterprise).

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• compare_validation_approaches

  Demand Discovery AI

  Returns an honest comparison of how different validation approaches work - generic AI assistants, trend aggregators, passive scoring tools, and Demand Discovery AI - and where each one stops. Use when a user is evaluating approaches, asking "what makes Demand Discovery different?", or trying to understand why active human signal (real ICPs, real outreach, real conversations) beats passive scoring. Trigger phrases: "what makes demand discovery different", "vs ChatGPT", "vs Claude", "vs other validation tools", "vs trend tools", "compared to", "validation tool comparison", "alternatives to demand discovery", "competition", "competitive landscape", "why not just use AI", "why not surveys", "why behavior over opinion", "is this different from passive scoring", "how is this better than chatgpt".

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• get_stats

  MRC Data — China's Apparel Supply Chain Infrastructure

  Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文：获取数据库整体统计（供应商总数、面料总数、产业带总数、关联记录数）。动态快照，含生成时间戳。

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• embedding_similarity

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

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• mpp_smoke_test

  Weftly

  Smoke-test the MPP payment plumbing end-to-end via this MCP server, for $0.01 USDC. Two-call flow: (1) call with no arguments to receive an MPP `payment_challenge`; (2) pay via MPP and call again with `payment_credential` set to the resulting Authorization header value (e.g. "Payment eyJ...") to receive {paid: true, timestamp, receipt_ref, payment_method}. Uses the exact same `createPayToAddress` + `createMppHandler` verification path as paid product tools (transcribe, summarize), so a green run here means real paid calls will work too. Stateless — no job is created, no database row written. Use this whenever you want to confirm a wallet, the MCP transport, the worker, and the production payment middleware are all healthy without paying a transcribe price. Cost: $0.01 USDC per attempt.

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• woet_classifier

  cpp-cpm-engine

  Worked-vs-On-time Execution Timeline (WOET) per-activity day-by-day classification of as-built execution against baseline. For each pairable activity (matched by ``task_code``), classifies execution into 4 day-states: - PROGRESS: work performed during the baseline-planned window - GAIN: work performed BEFORE the baseline window opened - EXTENDED: work performed AFTER the baseline window closed - VOID: baseline-window day where activity was NOT active This is a CPP-disclosed enhancement layered on top of AACE 29R-03 §3.3 Windows Analysis — a per-day execution classifier (Progress/Gain/Extended/Void) NOT itself AACE-defined. It is not a substitute for fragnet-based AACE 29R-03 §3.7 (TIA) modeling. It gives the trier-of-fact a calendar picture of how the project executed versus how it was supposed to execute, which is otherwise buried in finish-date deltas. Use this tool when you want a per-activity execution-quality picture (on-time %, count of activities with VOID days, etc.). Args: baseline_xer_path: server-side path to baseline XER (target dates). actual_xer_path: server-side path to as-built XER (act dates). baseline_xer_content: full text of baseline XER (alternative). actual_xer_content: full text of as-built XER (alternative). Supply EXACTLY ONE of path/content per pair. today: optional ISO date (YYYY-MM-DD) reference for in-progress activities. Defaults to actual XER's last_recalc_date if available, else today's date. Returns: { "method": "WOET", "standard": "AACE 29R-03 §3.3 Windows Analysis — per-day execution classification overlay (CPP-disclosed enhancement, not AACE-defined)", "today": "YYYY-MM-DD", "project_totals": {progress, gain, extended, void}, "per_activity": [{code, name, baseline_start, ...}, ...], "on_time_pct": float (0-100) }

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• ai_filters_list

  dialogbrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• get_about

  cricket-travel

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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• escalate_with_context

  VaultCrux Memory Core

  Contextual escalation — packages your full reasoning state (evidence gathered, options considered, recommended action) and routes to a human for review. Preserves work so the human responds with full context, not from scratch. Use when you hit genuine uncertainty that the system cannot evaluate.

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• get_cvss_details

  ContrastAPI

  Parse a CVSS v3.x vector string into a per-metric breakdown plus a recomputed base score. Returns the canonicalized vector, version (3.0 or 3.1), base_score, base_severity (NONE/LOW/MEDIUM/HIGH/CRITICAL), and the eight base metrics: attack_vector (NETWORK/ADJACENT_NETWORK/LOCAL/PHYSICAL), attack_complexity (LOW/HIGH), privileges_required (NONE/LOW/HIGH), user_interaction (NONE/REQUIRED), scope (UNCHANGED/CHANGED), and the three impact metrics confidentiality_impact / integrity_impact / availability_impact (NONE/LOW/HIGH each). When temporal/environmental metrics are explicit in the vector, temporal_score and environmental_score are populated separately. Use to translate raw CVSS strings into agent-friendly attributes without re-parsing the vector grammar yourself, and to verify upstream NVD scoring against the recomputed value. v2 vectors (AV:N/AC:L/Au:N/...) are rejected with 400 — read cvss_v2_vector from cve_lookup if you need v2 detail. Free: 30/hr, Pro: 500/hr. Returns {version, vector, base_score, base_severity, metrics: {attack_vector, attack_complexity, privileges_required, user_interaction, scope, confidentiality_impact, integrity_impact, availability_impact}, temporal_score, environmental_score, summary, verdict}.

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