"General search for the term 'good'" matching MCP tools:

• get_cost_estimate

  mental-health-care

  [Step 1 of cost_check] Returns the cost-estimate tool URL pre-filled with the user's insurance + service if provided, plus the general copay range. The tool URL is a hand-off — the user verifies their plan there for an exact copay. Use when: The user asks "how much does therapy cost?" / "is X insurance covered?" / "what's my copay?" — return both the general range AND the deep-link. Don't use when: The user wants to find a provider — use find_provider (which already filters by accepted insurance). Example: get_cost_estimate({ insurance: 'Aetna', service: '354092' })

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• search_documentation

  Hatchable

  Search Hatchable's own documentation for platform behavior — routing, the SDK surface, deploy semantics, auth config, runtime limits. Call this instead of guessing when you're unsure how a Hatchable feature works. Ranks results by term frequency across headed sections. Returns source file, section heading, and a snippet around the hit.

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• search_nodes

  Diagrams MCP

  Search for diagram nodes by keyword across all providers and services. For targeted browsing when you know the provider, use list_providers -> list_services -> list_nodes instead. Args: query: Search term (case-insensitive substring match). Returns: List of matching nodes with keys: node, provider, service, import, alias_of (optional). Sorted by relevance: exact match first, then prefix, then substring.

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• search_products

  TrustRails

  HOW TO CALL THIS TOOL — read before every call: Decompose the user's request into filters first. Only what's left over goes in query. STEP 1: brand name → brand filter. STEP 2: product category → category filter. STEP 3: price → min_price/max_price. STEP 4: what remains → query. BAD: query='Sony headphones under £200' | GOOD: brand='Sony', category='Headphones', max_price=200, no query. BAD: query='tablet' | GOOD: category='Tablets', no query. BAD: query='smartwatch' | GOOD: category='Wearables', no query. BAD: query='macbook neo' | GOOD: brand='Apple', category='Laptops', query='neo'. BAD: query='Samsung QLED TV' | GOOD: brand='Samsung', category='TVs', query='qled'. If brand+category alone cover what the user wants, omit query entirely. Only put differentiating terms in query: model lines (neo, ultra, oled), variants, model numbers (WH-1000XM5, s25 ultra). CROSS-CATEGORY NOTE: Gaming headsets → category='Headphones', query='gaming headset'. The Gaming category is consoles/controllers/accessories only. Always set lite=true. If 0 results, broaden the query or drop filters. Use get_product for full specs. Search 26,000+ deduplicated UK electronics products across multiple retailers with price comparison. Covers: Laptops, Desktops, Phones, Tablets, Headphones, Monitors, TVs, Cameras, Keyboards, Mice, Speakers, Gaming, Wearables, Printers, Networking, Storage, Audio, Drones, Cables & Chargers. All prices in GBP. Returns summary data: title, brand, price, availability, category, purchase link, offer_count. When offer_count > 1, call get_product for all retailer offers. For spec-based queries (RAM, ports, screen size, weight etc.), search first then call get_product on top 3-5 results — do not assume specs from titles. STOCK: When availability is out_of_stock, mention it as an alternative and suggest checking back — do not silently omit it.

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• generate_counteroffers

  zopaf

  Generate 3 counteroffers that are equally good for the user but structured differently. Present ALL THREE simultaneously to the counterpart — their reaction reveals what they care about. target_satisfaction: 'ambitious', 'moderate', or 'conservative'.

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• mental-health-get-test

  Well-Being Check

  Loads one supported self-assessment into the widget by slug. Use `gad7` for anxiety screening, `phq9` for depression screening, and `who5` for general well-being screening when the user wants to take one of those assessments.

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• planning_search

  UK Property Data

  Find the planning portal URL for a UK postcode. Returns council info and portal search URLs. Does not scrape planning applications -- use the returned URLs to search directly.

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• search_mesh

  PubMed / MEDLINE Literature Search

  Search the MeSH vocabulary for standardized medical terms. Find MeSH (Medical Subject Headings) descriptors to use in precise PubMed searches. Returns MeSH IDs, preferred terms, and scope notes. Args: term: Search term (e.g. 'diabetes', 'heart failure', 'opioid'). limit: Maximum results (default 10).

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• biobtree_search

  BioBTree

  Search 70+ biological databases. SYNTAX: biobtree_search(terms="entity") BEFORE SEARCHING - Use your training knowledge to plan: 1. What type of entity is this? (disease, process, drug, gene, protein) 2. What is the query asking for? (drugs, genes, function, etc.) 3. What equivalent terms might give better results? (e.g., "temperature homeostasis" is a process → related condition is "fever") 4. Choose best entry point for query type (disease terms for drug queries) WORKFLOW: 1. Search WITHOUT dataset filter first (discover where entity exists) 2. Use IDs from results with biobtree_map QUERY PATTERNS (choose based on question): "DRUG FOR DISEASE/CONDITION X": - Prefer disease terms (mesh/mondo/efo) over GO terms for drug queries - If search only returns GO term, search for the related CONDITION instead (e.g., "temperature homeostasis" → search "fever" instead) - Search disease → mondo → clinical_trials → chembl_molecule - OR search drug class directly (e.g., "antipyretic", "NSAID", "antibiotic") - Verify mechanism for top 2-3 drugs only (don't enumerate all proteins!) "DRUG TARGETS" (use BOTH paths for complete picture): - chembl: >>chembl_molecule>>chembl_target>>uniprot (mechanism-level) - pubchem: >>pubchem>>pubchem_activity>>uniprot (protein-level, often 50+ targets) - Filter approved: >>chembl_molecule[highestDevelopmentPhase==4] "DISEASE GENES": - Search disease → mondo/hpo → gencc/clinvar/orphanet → hgnc "PROTEIN FUNCTION": - Search protein → uniprot → go/reactome "MECHANISM QUERIES" (drug-disease): - Use biobtree_entry to see what's connected (xrefs) - Check EDGES to see where each xref leads - Follow connections relevant to your question - Build chain: Drug → Target → [connections] → Disease RETURNS: id | dataset | name | xref_count

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• ateam_github_patch

  ateam-mcp

  Edit a file in the solution's GitHub repo and commit. Two modes: 1. FULL FILE: provide `content` — replaces entire file (good for new files or small files) 2. SEARCH/REPLACE: provide `search` + `replace` — surgical edit without sending full file (preferred for large files like server.js) Always use search/replace for large files (>5KB). Always read the file first with ateam_github_read to get the exact text to search for.

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• browse_collections

  Remember Me Collections

  Browse published Bible verse collections. Search by keyword, filter by language, sort by popularity. Args: search: Search term to filter by name, description, or publisher name. language: Language code prefix (e.g. "en", "de", "ja", "zh"). ordering: Sort order: -downloads (default), -created, name. limit: Number of results (1-100, default 20). offset: Starting position for pagination.

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• thinkneo_usage

  ThinkNEO Control Plane

  Returns usage statistics for your ThinkNEO API key. Shows calls today, this week, this month, monthly limit, remaining calls, top tools used, estimated cost, and current tier. Works without authentication (returns general info).

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• cryptoguard_search

  CryptoGuard

  Search for a token's CoinGecko coin ID by name, symbol, or contract address. Use this first if you're unsure of the correct coin_id for scan_token or validate_trade. Example: search 'pepe' to find the correct coin ID.

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• confirm_pantry_fresh

  scraps-kitchen-mcp

  Reset the staleness clock on pantry items the user confirms are still good. Use when the user says items are fine, or after a pantry check. Get item IDs from get_pantry first.

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• baselight_search_tables

  baselight

  Search for tables using a text query and filters. Tables in Baselight have the following format: @username.dataset.table. Tables are grouped into datasets which can be public or private — you can search and use all public datasets as well as the user's private datasets. Search for tables directly when you are unable to find relevant datasets.

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• search_occupations

  O*NET Occupational Data

  Search O*NET occupations by keyword. Returns a list of occupations matching the keyword with their SOC codes, titles, and relevance scores. Use the SOC code from results with other O*NET tools to get detailed information. Args: keyword: Search term (e.g. 'software developer', 'nurse', 'electrician'). limit: Maximum number of results to return (default 25).

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• hemmabo_search_availability

  hemmabo-mcp-server

  Check whether a specific property is available for the requested dates. Use this tool after the user has selected a property from hemmabo_search_properties and wants to confirm availability before getting a quote. Do NOT use for general browsing — use hemmabo_search_properties instead. Returns available=true/false with conflict details (blocked dates, existing bookings, active locks) if unavailable.

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• agents_approve_draft

  DialogBrain

  Approve a pending agent draft and send the message. The draft will be sent to the conversation it was generated for. You can optionally edit the text before sending. Use this when user says: - 'Approve this draft' - 'Send this reply' - 'Approve and send' - 'Looks good, send it' IMPORTANT: This will send a message to a real person.

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• nexbid_categories

  discovery

  <tool_description> List all available product categories in the Nexbid marketplace with product counts. Optionally filter by country. </tool_description> <when_to_use> When user wants to explore what is available before searching. Use BEFORE nexbid_search to help narrow down the query. </when_to_use> <combination_hints> nexbid_categories → nexbid_search with category filter for targeted results. Good starting point for browse intent. </combination_hints> <output_format> List of categories with product counts. Optionally filtered by country. </output_format>

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• notes_save

  DialogBrain

  Save a note to your notebook. In agent mode writes to your own notebook by default; agents cannot write to other agents' notebooks. In MCP mode target_agent_id is required. If a note with the same key and scope already exists, it will be updated. Use scope to organize: 'global' for general knowledge, 'thread' for thread-specific context, 'person' for contact-specific info.

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