"CodeQL static analysis tool and semantic code analysis platform" matching MCP tools:

• search_creators

  Influship

  Find a creator by name/handle, while preserving legacy semantic creator search. Use this as the default creator lookup tool when the user gives a creator-ish string but not a canonical creator UUID: a handle, partial handle, display name, creator name, or profile-ish text. This is cheap, fast, and backed by the creator lookup index. If the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram"), prefer `get_profile` first because it returns the full platform profile. If you need to resolve a rough creator name or partial handle first, use this tool with `query_type: "creator_lookup"`. For backward compatibility, this tool still accepts the old semantic-search fields (`platforms`, follower/engagement filters, `creator_kinds`) and routes legacy calls to the semantic endpoint unless the query clearly contains a handle/profile URL. For new topical/niche discovery calls such as "fitness creators in NYC" or "vegan recipe creators with high engagement", prefer `semantic_search_creators` because its name is explicit and less likely to be confused with exact creator lookup. Examples: - User: "Find @cris" -> use this tool with query "cris" and query_type "creator_lookup". - User: "Who is that fitness coach called Jane?" -> use this tool with query "Jane" and query_type "creator_lookup". - User: "Pull @niickjackson on Instagram" -> use `get_profile` with platform "instagram" and username "niickjackson". - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool. Returns either autocomplete-style creator lookup results or legacy semantic results, depending on routing. Use returned creator IDs with `get_creator`, `find_lookalike_creators`, or `match_creators`; use returned platform usernames with `get_profile` or `get_posts`.

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• get_profile

  Influship Influencer Marketing MCP

  Fetch a single social profile by (platform, username). Always use this first when the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram") and you need the full profile: bio, follower/engagement metrics, recent activity, growth, and the canonical creator ID. Pass exactly the username they typed without the @ sign — case-insensitive matching is handled server-side. Do not use `search_creators` for an exact platform+username lookup. Examples: - User: "Pull @niickjackson on Instagram" -> use this tool with platform "instagram" and username "niickjackson". - User: "Tell me about instagram.com/niickjackson" -> parse the platform and username, then use this tool. - User: "Is @niickjackson a fit for Pixel?" -> use this tool first, then call `get_posts` and/or `match_creators` if the task needs content or fit analysis. Returns the profile record plus the underlying creator record. If you already have a creator UUID, use `get_creator` instead. For batch lookups by handle, use `lookup_profiles`.

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• lookup_profiles

  Influship Influencer Marketing MCP

  Batch-fetch up to 100 profiles by (platform, username) pairs. Use this when the user has a list of handles and you need profile data for all of them at once (e.g., "give me follower counts for these 30 accounts I'm considering" or "which of @a @b @c are real accounts?"). One round-trip beats 30 calls to `get_profile`. Use this for exact batch handle lookup, not semantic discovery. For one exact platform+username pair, use `get_profile`. For partial or fuzzy handle/name input, use `search_creators` or `autocomplete_creators`. Use `semantic_search_creators` only for topical/niche/audience discovery where false-positive semantic matches are acceptable. Examples: - User: "Compare @a, @b, and @c on Instagram" -> use this tool for the exact handle batch. - User: "Give me follower counts for these 30 accounts" -> use this tool. - User: "Find wellness creators in Austin" -> use `semantic_search_creators`, not this tool. The response splits results into `data` (profiles found) and `not_found` (the (platform, username) pairs that weren't recognized). Profiles are returned in no particular order — re-correlate via the platform/username fields if you need to preserve input order.

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• get_profile

  Influship

  Fetch a single social profile by (platform, username). Always use this first when the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram") and you need the full profile: bio, follower/engagement metrics, recent activity, growth, and the canonical creator ID. Pass exactly the username they typed without the @ sign — case-insensitive matching is handled server-side. Do not use `search_creators` for an exact platform+username lookup. Examples: - User: "Pull @niickjackson on Instagram" -> use this tool with platform "instagram" and username "niickjackson". - User: "Tell me about instagram.com/niickjackson" -> parse the platform and username, then use this tool. - User: "Is @niickjackson a fit for Pixel?" -> use this tool first, then call `get_posts` and/or `match_creators` if the task needs content or fit analysis. Returns the profile record plus the underlying creator record. If you already have a creator UUID, use `get_creator` instead. For batch lookups by handle, use `lookup_profiles`.

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• ado_analyze_workitem

  ALM X++ MCP Server

  AZURE DEVOPS ONLY -- Fetch a Work Item and assemble ALL technical context needed for D365 F&O expert analysis. [~] PRIORITY TRIGGER: 'analyse le workitem', 'analyse la tâche', 'analyse le FDD/RDD/CR/IDD', 'read the work item', 'check the bug', 'look at ticket', 'review task', '#1234', 'WI#', 'WI ', 'item #'. NEVER for: labels (@SYS/@TRX/@FIN), X++ code lookup, AOT objects -- use search_labels / search_d365_code instead. ## WHAT THIS TOOL RETURNS Raw structured context only -- NOT a finished analysis. The tool returns: 1. Work item metadata (title, description, repro steps, acceptance criteria, comments) 2. D365 standard KB object details: fields, methods, code snippets for every matched object 3. Custom code on disk (Aprolis extension): existing CoC methods, extension bodies 4. Chain of Command / relation graph for all impacted objects ## YOUR JOB AS COPILOT AFTER CALLING THIS TOOL You MUST synthesize the raw context into a precise developer-ready analysis IN FRENCH. Write it in a professional tone, as if authored by a senior D365 consultant -- no emojis, no icons. The analysis must contain these sections: 1. **Compréhension du besoin** -- résume ce que le client demande en 2-3 phrases claires 2. **Analyse technique** -- identifie la cause racine en croisant le besoin + les objets KB + le code custom 3. **Instructions de développement** -- liste ordonnée et précise : quel objet, quelle méthode, quoi modifier - Si une extension custom existe sur disque -> pointer exactement quelle méthode à modifier - Si pas d'extension -> indiquer quel CoC créer, sur quel objet standard, quelle méthode 4. **Estimation** -- chiffrage en heures/jours selon la complexité détectée 5. **Commentaire ADO** -- Texte markdown sans icônes, prêt à poster sur le WI analysé UNIQUEMENT. IMPORTANT: never post (never call ado_post_comment) on any linked/related work item -- only on the analyzed WI. Requires DEVOPS_ORG_URL + DEVOPS_PAT env vars.

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• generate_framework_analysis

  jeda-ai

  Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. Required for any framework analysis flow. The companion tool `recommend_framework` returns framework_ids that are valid here. Pre-discovered framework_ids (SWOT = "swot-analysis", PESTEL = "pestel-analysis", Porter's 5 Forces = "porters-five-forces", etc.) work without a recommendation step. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (slug) + user_inputs object. The user_inputs schema is per-framework; call `recommend_framework` first if you don't know the field set, OR send a `raw_prompt` as a free-text fallback. Example: { "framework_id": "swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }

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• atlas_get_analysis

  CareerProof MCP

  Retrieve a completed analysis result by analysis ID. Returns scores, competency breakdown, and recommendations. analysis_id comes from atlas_start_gem_analysis response or atlas_list_analyses. Only works after analysis is completed -- check with careerproof_task_status first. Free.

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• scan_skill

  SecurityScan

  Scan a GitHub repository or skill URL for security vulnerabilities. This tool performs static analysis and AI-powered detection to identify: - Hardcoded credentials and API keys - Remote code execution patterns - Data exfiltration attempts - Privilege escalation risks - OWASP LLM Top 10 vulnerabilities Requires a valid X-API-Key header. Cached results (24h) do not consume credits. Args: skill_url: GitHub repository URL (e.g., https://github.com/owner/repo) or raw file URL to scan Returns: ScanResult with security score (0-100), recommendation, and detected issues. Score >= 80 is SAFE, 50-79 is CAUTION, < 50 is DANGEROUS. Example: scan_skill("https://github.com/anthropics/anthropic-sdk-python")

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• malware_get_template

  website-search

  Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.

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• search_creators

  Influship Influencer Marketing MCP

  Find a creator by name/handle, while preserving legacy semantic creator search. Use this as the default creator lookup tool when the user gives a creator-ish string but not a canonical creator UUID: a handle, partial handle, display name, creator name, or profile-ish text. This is cheap, fast, and backed by the creator lookup index. If the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram"), prefer `get_profile` first because it returns the full platform profile. If you need to resolve a rough creator name or partial handle first, use this tool with `query_type: "creator_lookup"`. For backward compatibility, this tool still accepts the old semantic-search fields (`platforms`, follower/engagement filters, `creator_kinds`) and routes legacy calls to the semantic endpoint unless the query clearly contains a handle/profile URL. For new topical/niche discovery calls such as "fitness creators in NYC" or "vegan recipe creators with high engagement", prefer `semantic_search_creators` because its name is explicit and less likely to be confused with exact creator lookup. Examples: - User: "Find @cris" -> use this tool with query "cris" and query_type "creator_lookup". - User: "Who is that fitness coach called Jane?" -> use this tool with query "Jane" and query_type "creator_lookup". - User: "Pull @niickjackson on Instagram" -> use `get_profile` with platform "instagram" and username "niickjackson". - User: "Find news creators with 1M+ followers" -> use `semantic_search_creators`, not this tool. Returns either autocomplete-style creator lookup results or legacy semantic results, depending on routing. Use returned creator IDs with `get_creator`, `find_lookalike_creators`, or `match_creators`; use returned platform usernames with `get_profile` or `get_posts`.

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• licium_analyze_market

  Licium

  Analyze a prediction market question. Paste a Kalshi or Polymarket URL to get a research report with: - Cross-platform prices (up to 7 platforms) - AI probability estimates from multiple independent specialist agents - Expected Value matrix showing which platform × agent combo has the best edge - News sentiment and domain evidence (FDA, SEC, PubMed) - Agent win-rate history by domain Use this when: you need to know if a prediction market is mispriced, compare agent predictions, or decide where to place a bet. EXAMPLES: "https://kalshi.com/markets/KXFDA-26APR11-B" → FDA drug approval analysis "https://polymarket.com/event/will-trump-win-2028" → election analysis

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• ceevee_list_positioning_sessions

  CareerProof MCP

  List all positioning sessions (market analysis through lens selection to targeted edits). Returns an array of session objects with id, status, cv_version_id, and created_at. Use the session id with ceevee_get_positioning_session for full details including analysis results, edits, and PDFs. Free.

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• explain_greenfield

  SCModeling

  Reference text on greenfield analysis — clean-slate facility-location math. Covers the weighted center-of-gravity (Weber) formulation, Weiszfeld's iterative algorithm, Lloyd's-style alternating location-allocation for N facilities, service constraints (% demand vs % customers within a distance band), and the inverse problem of solving for minimum N. Also covers when to use greenfield vs facility selection (the open/close MIP). Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does greenfield analysis work' or 'where would I put my DCs' question. ChiAha's GreenfieldAnalysis engine powers the US Greenfield Design demo on the sandbox.

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• create_use_case

  mcp-server

  Creates and saves a new use case (reusable analysis). **When to use this tool:** - When the user asks to "save this analysis", "create a use case", "remember this query" - After building a SQL query the user wants to reuse - To capitalize on a recurring business analysis **Available scopes:** - 'member' (default): Personal use case, visible only to you - 'project': Shared with the entire project team (requires project_id) **Best practices:** - Slug: technical identifier in snake_case (e.g., weekly_campaign_performance) - Name: human-readable name (e.g., "Weekly Campaign Performance") - Description: explain the business context and when to use this analysis - SQL template: include the SQL query if it's generic and reusable

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• scope_get_deliverable

  Scope (Legal)

  Fetch a single vendor-uploaded deliverable by id. Returns metadata + a short-lived signed download URL (1 hour TTL). The buyer's AI can hand the URL to a downstream analysis tool (transcript review, exhibit extraction, etc.) - Scope is the delivery layer, not the analysis layer.

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• licium_analyze_market

  Licium

  Analyze a prediction market question. Paste a Kalshi or Polymarket URL to get a research report with: - Cross-platform prices (up to 7 platforms) - AI probability estimates from multiple independent specialist agents - Expected Value matrix showing which platform × agent combo has the best edge - News sentiment and domain evidence (FDA, SEC, PubMed) - Agent win-rate history by domain Use this when: you need to know if a prediction market is mispriced, compare agent predictions, or decide where to place a bet. EXAMPLES: "https://kalshi.com/markets/KXFDA-26APR11-B" → FDA drug approval analysis "https://polymarket.com/event/will-trump-win-2028" → election analysis

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• get_launch_context

  mcp-server

  Retrieves the full context of a Quanti launch session. The user has pre-configured an analysis from the Quanti interface and was redirected here with a launch_id. Call this function to get the analysis details to execute (name, prompt or SQL template, project).

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• google_ads_render_search_terms_analysis

  paid-ads

  Preferred user-facing Google Ads search-terms analysis tool. Renders the search-terms analysis dashboard and can either take analysisPayload from google_ads_analyze_search_terms or fetch the analysis directly when called with search-term-analysis arguments.

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• usaspending_spending_by_category

  usaspending-mcp-server

  Aggregate federal spending grouped by a specific dimension: NAICS industry code, PSC product/service code, awarding agency, funding agency, CFDA assistance program, or recipient. Returns top items with obligation amounts — useful for trend and breakdown analysis. Chain NAICS codes into usaspending_search_awards filters or usaspending_autocomplete lookups.

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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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