"How to work with Google Sheets" 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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• rating_get_sheet

  website-search

  Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. 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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• extract_contract_from_url

  transaction-coordinator

  Extract structured transaction data from a contract at a URL. Downloads the document, extracts text (with OCR fallback for scanned PDFs), and runs PrimaCoda's contract-extraction prompt to return parties, addresses, dates, prices, and key contract fields. Use this when an agent has the contract hosted somewhere (Dropbox, Google Drive direct download, Square Space, etc.) and wants to skip the upload step. For multi-document deals (purchase + addenda + disclosures), use the PrimaCoda dashboard's batch upload — this tool handles ONE document. Args: pdf_url: Direct download URL for the contract (PDF, DOCX, TXT, or image). Must be reachable from the PrimaCoda server. Google Drive "shared link" URLs work if set to "anyone with link"; other share URLs may need their direct-download form. api_key: Your PrimaCoda MCP API key (starts 'pck_').

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• google_ads_list_accessible_customers

  paid-ads

  Lists directly accessible Google Ads customers for the configured Google Ads credentials, including descriptive names when Google returns them. Use this to discover customer IDs before running Google Ads hierarchy or reporting tools.

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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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• 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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• 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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• 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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• 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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• 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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• resolve_maps_urls

  Google Maps

  Resolves a list of Google Maps URLs into canonical Google Maps Place IDs. **When to call this tool (CRITICAL):** * Use this tool when the user provides one or more Google Maps sharing links or URLs (e.g. 'https://maps.app.goo.gl/...', 'https://www.google.com/maps/place/...', or 'https://maps.google.com/...') and you need to extract the underlying canonical Place IDs. * You can specify up to 20 URLs to resolve in a single batch request. **Input Requirements (CRITICAL):** * **`urls` (array of strings - MANDATORY):** The list of Google Maps URLs to resolve. Each URL must be a valid, single-place Google Maps URL. **Error Handling (CRITICAL):** * This is a batch processing tool. A request might return "mixed results" (e.g. some URLs resolve successfully while others fail). * The output list of `entities` is guaranteed to map 1:1 with the input `urls` indices. A failed URL resolution will result in an empty `Entity` message (no fields are set) at its corresponding index in the `entities` list. * You **MUST** check the `failed_requests` map field in the response to identify which specific URL index failed. The key of `failed_requests` represents the 0-based index of the failed URL in the request. Do not assume the entire batch call failed because of a partial failure.

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• createDeepResearch

  Parallel Task MCP

  Creates a Deep Research task for comprehensive, single-topic research with citations. USE THIS for analyst-grade reports, NOT for batch data enrichment. Use Parallel Search MCP for quick lookups. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed. Multi-turn research: The response includes an interaction_id. To ask follow-up questions that build on prior research, pass that interaction_id as previous_interaction_id in a new call. The follow-up run inherits accumulated context, so queries like "How does this compare to X?" work without restating the original topic. Note: the first run must be completed before the follow-up can use its context.

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• request_revision

  Human Pages

  Request changes on submitted work (job must be SUBMITTED). Moves job back to ACCEPTED so the human can resubmit. Include a clear reason explaining what needs fixing. The human receives a notification. Use approve_completion instead if the work is satisfactory.

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• get_validation_framework

  Demand Discovery AI

  Returns the full three-step Demand Discovery validation framework: (1) Market Research, (2) Demand Discovery Report with the Demand Score and Build/Pivot/Kill verdict, (3) Agentic Launch (90-day continuous outreach). Use when a user asks "how do I validate an idea?", "what's the methodology?", or wants to understand the structured approach. Built on the "behavior over opinion" principle. Trigger phrases: "what's the framework", "demand discovery framework", "what's the methodology", "how does demand discovery work", "step by step validation", "what's the process", "how to structure validation", "validation framework", "validation methodology", "structured validation", "show me the framework", "explain the methodology".

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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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• rating_load_context

  website-search

  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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• 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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• orcid_get_works

  orcid-mcp-server

  Retrieve works associated with an ORCID iD — publications, datasets, software, preprints, and more. Returns work summaries with put-codes, titles, types, publication dates, journal names, and all external identifiers (DOIs, PMIDs, arXiv IDs, ISBNs). Pass the put_code from each work to orcid_get_work_detail to retrieve the full record including abstract and contributors. External IDs are ready for chaining to Crossref, PubMed, or arXiv servers. Works are self-reported; a researcher may not have linked all their publications.

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• ado_post_comment

  ALM X++ MCP Server

  WHEN: user explicitly asks to post, add, or save a comment to an ADO Work Item. [~] PRIORITY TRIGGER: call AFTER `ado_analyze_workitem` when user says 'post the analysis', 'save it to the ticket', 'ajoute en commentaire'. WARNING: ALWAYS ask for explicit user confirmation before calling this tool. Recommended workflow: (1) call `ado_analyze_workitem`, (2) show analysis to the user, (3) ask 'Shall I post this comment to Work Item #X?', (4) only then call this tool. Requires DEVOPS_ORG_URL + DEVOPS_PAT with Work Items: Write permission.

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• log_progress

  VaultCrux Memory Core

  Receipted write-through to PlanCrux's log endpoint. Appends a structured log entry to a task with optional evidence references and stage binding. Cannot change task or stage status (human-only), but records work done, findings, and blockers encountered.

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