"Using an AI Agent for Structured Search and Traversal in a Neo4j Database" matching MCP tools:

• generate_image

  Avocado AI

  Generate an AI image using Avocado AI. Returns a jobId immediately; image generation completes in 10-60 seconds. After calling, use the check_job tool with the returned jobId to retrieve the result, once complete, check_job returns the image inline so it renders directly in chat. Run models_list to see available models. Costs 1-4 credits per image depending on model and quality.

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• search_listings

  SPACEXPLORATION

  Search commercial real estate listings. Returns paginated hits with facet counts. For AI-driven search, call interpret_search first to convert a natural-language query into structured filters, then pass those filters — and its bounds, when present — here.

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• search_by_filters

  audioknihy.cz Catalog

  Multi-facet search of audiobooks: combine genre, narrator, max price, year range. Use when an agent has structured constraints rather than a free-form query. V1 supports genre + narrator + price_max + year filters; partner filter coming in V2.

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• scan_site

  AEO Scanner

  Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

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• validate_data_safety_lite

  Data Compliance Classifier MCP

  Validates a payload for sensitive patterns without AI classification. Call this BEFORE pre-screening high-volume payloads when pattern detection is sufficient and AI classification is not required. Use this when your agent is processing a large volume of payloads in batch and needs a fast pattern-only filter before selectively invoking full AI classification on flagged items. Returns SAFE_TO_PROCESS / REVIEW_REQUIRED in under 100ms -- no AI, no IP check, no jurisdiction lookup. Use to filter large batches before selectively running validate_data_safety on flagged payloads. Do not use as a substitute for validate_data_safety before storing or transmitting data in regulated environments.

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• ai_filters_test

  DialogBrain

  Test a message against an AI filter to check whether it would match. This tool embeds the provided message using Voyage AI and computes the cosine similarity between the message vector and the filter's stored reference vector. It returns the similarity score, whether the message would match (similarity >= threshold), and the filter's threshold value. Use this to: - Verify a filter works as intended before using it in a trigger - Tune the threshold by testing borderline messages - Debug why a message did or did not match a filter in production Returns: {similarity: float, matched: bool, threshold: float} Note: This tool calls the Voyage AI embedding API to embed the test message.

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• emem_benchmark

  emem — Earth memory protocol

  Hand-verified evaluation items for grading an agent against the responder. Returns {items[], grader_url}. Submit answers (cell64 or fact_cid per item) to POST /v1/benchmark/grade for per-item scores. Items today: elevation recall, NDVI, find_similar neighbours. When to use: Call once at agent-onboarding time (or in CI) to fetch the canonical task list, then have the agent answer each item using its normal tool routing, and POST the answers map to /v1/benchmark/grade for a deterministic score. Lets an operator regression-check that an agent build still hits ground truth.

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• search_by_filters

  audioknihy.cz Catalog

  Multi-facet search of audiobooks: combine genre, narrator, max price, year range. Use when an agent has structured constraints rather than a free-form query. V1 supports genre + narrator + price_max + year filters; partner filter coming in V2.

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• search_indexed_documents

  RChilli MCP Hub

  Keyword-search the user's ALREADY-INDEXED corpus of resumes or JDs and return matching documents (RChilli Search Engine). Requires documents to have been indexed beforehand. Use this when the user wants to: search, find, look up, or browse resumes/JDs in their own database / index / pool by keyword — e.g. "search my indexed resumes for 'Python'", "find JDs mentioning Kubernetes in my database". Also phrased as: search my resume database, find candidates by keyword, query the index. Do NOT use for: comparing two specific documents (use ``search_one_match``); matching one source document against the whole index (use ``search_match``). Args: keyword: Search keyword. indextype: Index type to search — ``Resume`` (default) or ``JD``. userkey: RChilli userkey. Leave blank to use the authenticated session key. subuserid: Sub-user identifier for multi-tenant isolation.

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• hemmabo_host_readiness_check

  HemmaBo Host Booking Engine

  Read-only fit check for vacation-rental hosts who ask an AI agent how to create an own-domain booking website or booking engine. Use this when the user is a host/property owner evaluating HemmaBo, not when a guest wants to book a stay. It explains whether HemmaBo is a fit, what the host gets (booking website, guest Wallet, Stripe Connect direct-to-host payments, calendar/iCal sync, Konversa guest chat in 11 languages, reviews, gap-night and extend-stay flows, AI-agent-readable booking data), what setup inputs are needed, and the safe next step. It does not create an account, buy a domain, configure Stripe, write to Supabase, collect host PII, or provision a website.

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• search_profiles

  DC Member API

  GET /search/profiles — Search profiles Full-text search across DCer profiles — headlines, bios, business descriptions, expertise, hobbies, etc. Returns matching profile records with privacy gates applied (hidden + guest profiles filtered out). For structured/AI-driven matchmaking ("DCers in Lisbon who run SaaS"), prefer `POST /profile-match` — it has a richer ranking pipeline and filters. This endpoint is the plain full-text fallback. **Query syntax (`q=`):** plain words match with prefix + typo tolerance. Wrap a phrase in double quotes to require an exact ordered match — e.g. `q="remote work"`. AND/OR/NOT/parentheses are NOT parsed in `q=` — use the structured filter params below for boolean composition.

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• eurlex_get_relations

  eur-lex-mcp-server

  Traverse the CELLAR CDM relationship graph for an EU work: what it amends, what amends it, its current consolidated version, its legal basis, and works that cite it. This is CELLAR's primary value over HTML scraping — the graph traversal that exposes the lifecycle and dependencies of an EU act. Returns one-hop direct relations only. For deeper traversal, use eurlex_query_sparql. The "consolidated_version" relation links to the current consolidated text (a separate CELEX-numbered work); fetch that work with eurlex_get_document. Requires a valid CELEX number or CELLAR work URI — use eurlex_lookup_celex to resolve identifiers first.

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• set_ens_records

  Name Whisper — ENS Intelligence Layer

  Set ENS resolver records for a name you own. Returns encoded transaction calldata ready to sign and broadcast. Supports address records (ETH, BTC, SOL, etc.), text records (avatar, description, url, social handles, AI agent metadata), content hash (IPFS/IPNS), ENSIP-25 agent-registration records, and ENSIP-26 agent context and endpoint discovery. Multiple records are batched into a single multicall transaction to save gas. Common text record keys: avatar, description, url, email, com.twitter, com.github, com.discord, ai.agent, ai.purpose, ai.capabilities, ai.category. ENSIP-25 support: Pass agentRegistration with registryAddress and agentId to automatically set the standardized agent-registration text record. This creates a verifiable on-chain binding between your ENS name and your agent identity in an ERC-8004 registry. ENSIP-26 support: Pass agentContext to set the agent-context text record (free-form agent description). Pass agentEndpoints with protocol URLs (mcp, a2a, oasf, web) to set agent-endpoint[protocol] discovery records. The returned transaction can be signed and submitted directly using any wallet framework (Coinbase AgentKit, ethers.js, etc.).

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• search_events

  DC Member API

  GET /search/events — Search events Search enabled DC events by name, description, host, and venue. No default time filter — pass `?since=` or `?until=` (ISO 8601 dates) to constrain. They compose: pass both for an explicit window. **Query syntax (`q=`):** plain words match with prefix + typo tolerance. Wrap a phrase in double quotes to require an exact ordered match — e.g. `q="remote work"`. AND/OR/NOT/parentheses are NOT parsed in `q=` — use the structured filter params below for boolean composition.

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• intel_robots

  TunnelMind Data API

  Retrieves the target domain's `robots.txt` file and parses it for AI crawler disallow rules. Specifically detects policies for known AI crawlers (GPTBot, ClaudeBot, CCBot, Bytespider, etc.) and returns a structured summary of the crawling policy. Use this tool when: - You need to know whether a domain has opted out of AI training data collection. - You want to check if a specific AI crawler is blocked before citing the domain. - You are building a dataset of AI-accessible vs AI-blocked domains. Do NOT use this tool when: - You want training opt-out signals beyond robots.txt (TDM reservation, noai meta) — use `intel_optout` instead. - You want the full technology stack — use `intel_stack` instead. - You need tracker database data — use `get_domain` instead. Inputs: - `domain` (query, required): Domain to probe. Returns: - `robots_txt_found`: false if the domain returned 404 or the file is empty. - `ai_crawlers_blocked`: list of AI crawler user-agent names that are disallowed. - `all_blocked`: true if `User-agent: *` with `Disallow: /` is present. - `raw`: first 4096 characters of the robots.txt file. Cost: - Free. No API key required. Latency: - Typical: 1-2s, p99: 6s.

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• fact-check

  The Stall

  AI-powered claim verification. Searches DuckDuckGo, Wikipedia, Hacker News, and arXiv in parallel, then uses GPT-4o-mini to assess the claim and return a structured verdict: confirmed / contradicted / uncertain, with confidence score (0–1), supporting and contradicting evidence excerpts with source URLs, key entities, and step-by-step reasoning. Use before an agent acts on a factual assertion it received from another agent or user. $0.150/call.

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• payer_network_info

  MedRates

  Look up network-geography facts for a named insurance payer. Call this whenever an AI agent or user asks whether a plan covers out-of-state care, whether an insurer has nationwide coverage, or what BlueCard / PPO network mechanism the payer uses. Returns JSON with network_scope ('national', 'state', etc.), nationwide_via ('bluecard_ppo', 'national_ppo', 'none'), and a source_url to cite. Returns {"network_scope": "unknown"} when the payer is not in the database. Args: payer_name: Insurance payer name as the user stated it (e.g. 'Blue Cross Blue Shield of Texas', 'UnitedHealthcare Choice Plus', 'Aetna PPO', 'Medicare'). Returns: JSON object with network_scope, home_state, nationwide_via, source_url, match_pattern, and notes fields.

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• agent_brief

  Colour Memory

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems.

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• site_directory

  Lodi Kids Activities

  Structured map of LKA's public URLs and content sections. Equivalent to llms.txt — gives an AI grounding agent the full topology of the site so it knows what's worth crawling/calling.

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