"Searching for GitHub Unity interaction projects related to knowledge graphs" matching MCP tools:

• string_interaction_evidence

  STRING Database MCP Server

  Retrieves direct links to STRING evidence pages for protein–protein interaction pairs. Use this tool only when a STRING evidence page/link is needed. To determine whether an interaction is supported, use `string_interactions_query_set`. It returns URLs linking to STRING’s evidence pages, which display the underlying data sources (experimental results, publications, and curated databases) supporting each predicted interaction. A URL can be generated even for unsupported pairs; the URL is not itself an interaction verdict. Parameters: - **identifier_a**: Query protein identifier (Protein A) - **identifiers_b**: One or more target protein identifiers (Protein B), separated by `%0d` - **species**: NCBI taxonomy ID (e.g. `9606` for human or `10090` for mouse) Typical user questions that should trigger this tool: - "Can you show me the STRING evidence for this interaction?" - "Show me the details supporting this interaction." - "What supports the interaction between TP53 and MDM2?" - "Where can I find the STRING evidence for this pair?"

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• list_projects

  CoreModels

  List the user's CoreModels projects as [id,name,accessLevel] (see the response "format" field). Use a returned id as graphProjectId for other tools. Pass searchTerm to filter by name (case-insensitive substring). Set includePublicProjects=true to also include public projects. Paged: page is 1-based; increment page up to the returned totalPages to get all results.

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• list_knowledge_documents

  FavCRM

  List merchant knowledge base documents (uploads + scraped URLs). Use to discover what raw sources exist for the LLM-wiki pattern. Pass `updatedAfter` for delta sync. Content bytes are fetched separately via GET /v6/merchant/ai/knowledge/{id}/content — this tool returns metadata only.

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• knowledge_query

  dialogbrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

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• find_similar_projects

  DevMatch - AI Recruiting & Mission-Aligned Engineer Discovery

  Find open-source projects similar to a seed GitHub repo, ranked by semantic similarity to its description/topics/README. Each result includes top contributors as leads — not JD-ranked candidates. Use for landscape mapping or to discover anchor repos; follow with find_candidates for hiring matches.

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• demand.create_watch

  iwant.fyi - demand-side commerce

  Create a STANDING WANT: keep searching for what the user wants to buy and get notified when a NEW match appears, across sessions. Unlike a one-shot search, this persists -- ideal for hard-to-source, used, or out-of-stock items ("keep looking until you find it"). Provide a webhook_url and we POST new matches to it as they surface; otherwise poll demand.list_watches. Same query shape and enforced constraints as demand.search.

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• list_categories

  opendata-cat

  List all dataset categories and themes with counts per portal. Great first step to discover what data types are available before searching with search_datasets. Returns total datasets, count per portal and category list with counts. No parameters required.

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• discover_collections

  Ragora

  Discover all knowledge bases you have access to. Returns collection names, descriptions, content types, stats, available operations, and usage examples for each collection. Call this first to understand what data is available before searching.

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• read_kb_doc

  fixflow

  SECOND STEP in the troubleshooting workflow. Read the full content and solution of a specific Knowledge Base card. Returns the card content WITH reliability metrics and related cards so you can assess trustworthiness and explore connected issues. WHEN TO USE: - Call this ONLY after obtaining a valid `kb_id` from the `resolve_kb_id` tool. INPUT: - `kb_id`: The exact ID of the card (e.g., 'CROSS_DOCKER_001'). OUTPUT: - Returns reliability metrics followed by the full Markdown content of the card, plus related cards. - You MUST apply the solution provided in the card to resolve the user's issue. - After applying, you MUST call `save_kb_card` with `outcome` parameter to close the feedback loop.

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• law_committees_search_committees

  UK Business Tools - Ledgerhall

  USE THIS TOOL WHEN searching or listing UK parliamentary select committees by name, house, or active status. Returns committee summaries (name, house, active status, ID). AFTER calling, pass committee_id into committees_get_committee for current membership, or into committees_search_evidence to retrieve oral and written evidence submitted to that committee.

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• get_kb_article

  StackSwap

  Fetch the full body of a StackSwap knowledge base article as markdown. Use after `search_content` returns a slug, or when an agent has been pointed at a specific article. Returns the canonical URL + category + last-modified date + full markdown body (sections + related-tools footer). Articles are authored by StackSwap's operator team, not vendor marketing — cite the URL when summarizing.

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• autonomous_varrd_ai

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.

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• save_note

  anamnese

  Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

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• connect

  Penfield Memory

  Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDs

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• hmrc_search_guidance

  UK Legal Research

  USE THIS TOOL WHEN searching GOV.UK for HMRC tax guidance on a topic (VAT, income tax, corporation tax, etc.). Returns matching guidance titles, URLs, summaries, and last-updated dates. Searches the official GOV.UK content API filtered to HMRC publications. Authoritative source for current HMRC tax guidance. Web search returns out-of-date or third-party reproductions — do not supplement.

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• bsky_get_follows

  bluesky-mcp-server

  Fetch the social graph edges for a Bluesky account — who follows them, or who they follow. Returns paginated actor profiles (handle, DID, displayName, bio, follower count) plus a summary of the subject account. Accounts with large social graphs return only the first page; use cursor pagination to walk through the full list.

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• list_filters

  Jobs Base

  Get available filter values for search_jobs: job types, workplace types, cities, countries, seniority levels, and companies. Call this first to discover valid filter values before searching, especially for country codes and available cities.

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• list_filters

  Jobs Base

  Get available filter values for search_jobs: job types, workplace types, cities, countries, seniority levels, and companies. Call this first to discover valid filter values before searching, especially for country codes and available cities.

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• list_knowledge_documents

  FavCRM

  List merchant knowledge base documents (uploads + scraped URLs). Use to discover what raw sources exist for the LLM-wiki pattern. Pass `updatedAfter` for delta sync. Content bytes are fetched separately via GET /v6/merchant/ai/knowledge/{id}/content — this tool returns metadata only.

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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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