"A tool or software called Blender" matching MCP tools:

• analyze_track

  StudioSphere Pulse — Audio Intelligence

  Run audio analysis on a public audio URL. Requires estimate_cost to be called first (job_estimate_id). Requires PULSE_API_KEY. Before calling, you MUST confirm with the user that they have a lawful basis to submit this audio for analysis. For a user-requested folder, project, playlist, or batch, one confirmation can cover every track in that scope. Returns job_id — poll get_job_status for results.

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• submit_feedback

  Kifly — Agentic Commerce & Payments

  Send structured feedback to the Kifly team. **Call after a confusing response, a dead-end, or a successful workaround you had to invent** — it's how we improve the agent surface. Fire-and-forget: returns 202 immediately, no blocking, safe to skip if it would add latency to a user-facing flow. `category` and `severity` are required enums (don't free-form them). Include `context` with what you were doing (tool called, query used, response shape, what you expected). Add `suggested_fix` only if you have a concrete idea. Rate-limited to 10/min per agent token; everything is reviewed before influencing anything.

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• how_munimji_helps

  HelloBooks AI Agents MCP Server

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

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• tool_search

  Power Automate MCP Server by Flow Studio

  PREFER THIS over guessing tool names when picking from this server. Searches Flow Studio MCP tools by keyword, skill bundle, or explicit selector and returns full JSON schemas for matched tools so they can be called immediately. Call this whenever the user request maps to functionality you are not 100% sure about, OR when you want to load a whole skill bundle (build-flow, debug-flow, monitor-flow, discover, governance) at once. Query forms: (1) "skill:<name>" — fetch the full bundle (use list_skills first to see options); (2) "select:name1,name2" — fetch exact tools by name; (3) free-text keywords like "cancel run" or "trigger url" — ranked match against tool name + description. Non-billable.

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• autocomplete_creators

  Influship Influencer Marketing MCP

  Autocomplete creator names, usernames, or display names from partial input. Use this for fast lookup when the user types a partial handle or name and you need to resolve it to canonical creator IDs (e.g., "find @cris" or "who's that fitness coach called Jane?"). Cheap and fast — prefer over `search_creators` for handle-style queries where the user already knows roughly who they want. Use `get_profile` instead when the user gives an exact platform+username pair. Use `search_creators` for the same fuzzy creator lookup behavior with a less typeahead- specific name. Use `semantic_search_creators` only for discovery by topic, niche, audience, geography, or content style, not for resolving a known creator. Examples: - User: "Who is that fitness coach called Jane?" -> use this tool. - User: "Find @cris..." -> use this tool to resolve the partial handle. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. Returns a short list of matching creators with their IDs, platforms, and display names. Use the IDs returned here as input to `get_creator`, `find_lookalike_creators`, or `match_creators` for downstream operations.

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• submit_feedback

  Kifly — Agentic Commerce & Payments

  Send structured feedback to the Kifly team. **Call after a confusing response, a dead-end, or a successful workaround you had to invent** — it's how we improve the agent surface. Fire-and-forget: returns 202 immediately, no blocking, safe to skip if it would add latency to a user-facing flow. `category` and `severity` are required enums (don't free-form them). Include `context` with what you were doing (tool called, query used, response shape, what you expected). Add `suggested_fix` only if you have a concrete idea. Rate-limited to 10/min per agent token; everything is reviewed before influencing anything.

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• govcon_search_contract_awards

  DataNexus MCP

  Search government contract awards by keyword, agency, and date range. keyword: Contract scope e.g. "cybersecurity software". agency: Awarding agency e.g. "Department of Defense". Optional. date_from: Earliest award date ISO 8601 e.g. "2024-01-31". Optional. jurisdiction: "US", "EU", or "UK". Default "US". Returns: award amounts, recipient vendors, NAICS codes, award dates. Use govcon_fetch_vendor_contract_history for all contracts by a specific vendor. Use govcon_fetch_open_solicitations for active bids, not past awards. Source: USASpending.gov + SAM.gov. 4-hour cache. Example: search_contract_awards(keyword="cybersecurity software", agency="Department of Defense")

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• report_problem

  local-mcp

  Report a problem, feature request, or integration request to the LMCP team. IMPORTANT: Do NOT call this tool automatically. ALWAYS ask the user first: "Would you like me to report this issue to the LMCP team?" Only call this tool if the user explicitly agrees. When called without confirm=true, returns a preview of the anonymous data that will be sent. Show this preview to the user and only set confirm=true after they approve. No personal data is included — only version, OS, and permission status. Use type='feature' when the user wants a new capability. Use type='integration' when the user wants to connect an unsupported app.

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• linux_os

  ZEN SecDB

  List supported Linux operating systems and their corresponding versions for use with the `linux_audit` tool. ## What this tool does Returns an array of supported OS/version pairs, each in the form: {"os":"name", "versions":["version or codename"]} This allows the LLM and the user to know exactly which inputs are valid for the `linux_audit` tool. ## When to use this tool Use this tool when: - the user does not know which OS names or versions are supported - the user provides unclear or ambiguous OS information - you need to validate `os`/`version` before performing a Linux audit This tool should typically be called **before `linux_audit`** whenever parameters are uncertain. ## Inputs This tool does not require any input. ## Outputs Returns an array of objects: - **os**: supported Linux distribution identifier - **versions**: corresponding list of supported release or codename Example: [ {"os": "ubuntu", "versions": ["noble","focal"]}, {"os": "debian", "versions": ["bookworm","sid"]}, {"os": "redhat", "version": ["redhat-9.0"]} ] ## LLM usage guidelines - Use this tool to validate or suggest correct OS/version combinations before calling `linux_audit`. - If the user provides invalid or misspelled OS names, retrieve the official list here and ask them to select one. - Do not guess operating system identifiers-always rely on this tool to confirm correctness.

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• tm_export_video

  Twinmotion MCP

  Prepare a model for an animated walkthrough / video export by verifying the manifest is complete, then starting a secondary Model Derivative job that produces OBJ geometry (suitable for ingestion into offline rendering pipelines, Blender, or Unreal Engine). Also returns the list of available named views so the operator can stitch them into a camera path. Does NOT itself produce an mp4 — video encoding happens in the downstream UE/Twinmotion pipeline. When to use: when a user wants a walkthrough/flythrough video of a BIM model (e.g. 'make a 30-second tour of Tower A') — this tool gets the geometry into a UE-ingestible form (.obj, plus suggests FBX/glTF/USD naming like TowerA_walkthrough.fbx for the exported asset) and enumerates named views to guide camera path authoring. When NOT to use: not to actually encode video (no runtime renderer in this worker — output must be finished in Unreal/Twinmotion/Blender), not before tm_import_rvt, not if the manifest is still 'inprogress' (the tool will short-circuit and return status='pending'). Not for still images (use tm_render_image) or clash animations (use navisworks-mcp). APS scopes required: data:read data:write viewables:read. Write scopes are needed because this kicks off a new Model Derivative translation job (OBJ + thumbnail). Rate limits: APS default ~50 req/min; Model Derivative translation jobs ~60 req/min. OBJ derivatives of large BIM models can be multi-GB and take 10–45 min — rely on manifest polling with exponential backoff, not re-calling this tool. Errors: 401/403 = token/scope (data:write commonly missing); 404 = URN not found; 409 = OBJ derivative already queued (treat as success); 422 = input format does not support OBJ output (some IFC variants / proprietary formats — fall back to FBX/glTF via a different derivative format); 429 = back off 60s; 5xx = APS upstream. Side effects: STARTS a new translation job on an existing URN (consumes APS cloud credits). Writes usage_log. NOT idempotent per-call (each call creates a new job record), but APS will dedupe identical output requests internally if manifest already contains the derivative.

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• complete_checkout

  Custom Blinds Shop

  Phase 2 of 2. Finalise a checkout and mint the payment link — Yoco for card payments or Ozow for instant EFT. Returns payment_url to share with the customer. Payment confirmation arrives via webhook; poll get_order afterwards to confirm paid status. Once called, the checkout is locked — use cancel_checkout to abort if the customer changes their mind before paying.

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• 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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• mg_calculator_market_cap_sync

  Marketgenius

  Sync user-entered field values of the open Market Cap Calculator back to the session store so the model can read them via the state tool. Called by the View after any field change; hidden from the model.

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• get_investment_category_signal

  Stratalize Healthcare

  Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.

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• shakespeare_insult_generate

  Shakespeare Insult

  Generate a Shakespearean insult; optionally target a specific person or recipient category (colleague/ex/traffic/software/abstract_concept/the_universe), set severity (mild→nuclear), and request a modern English translation alongside the original.

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• autocomplete_creators

  Influship

  Autocomplete creator names, usernames, or display names from partial input. Use this for fast lookup when the user types a partial handle or name and you need to resolve it to canonical creator IDs (e.g., "find @cris" or "who's that fitness coach called Jane?"). Cheap and fast — prefer over `search_creators` for handle-style queries where the user already knows roughly who they want. Use `get_profile` instead when the user gives an exact platform+username pair. Use `search_creators` for the same fuzzy creator lookup behavior with a less typeahead- specific name. Use `semantic_search_creators` only for discovery by topic, niche, audience, geography, or content style, not for resolving a known creator. Examples: - User: "Who is that fitness coach called Jane?" -> use this tool. - User: "Find @cris..." -> use this tool to resolve the partial handle. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. Returns a short list of matching creators with their IDs, platforms, and display names. Use the IDs returned here as input to `get_creator`, `find_lookalike_creators`, or `match_creators` for downstream operations.

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• complete_checkout

  Custom Blinds Shop

  Phase 2 of 2. Finalise a checkout and mint the payment link — Yoco for card payments or Ozow for instant EFT. Returns payment_url to share with the customer. Payment confirmation arrives via webhook; poll get_order afterwards to confirm paid status. Once called, the checkout is locked — use cancel_checkout to abort if the customer changes their mind before paying.

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• cite

  dynamic-feed

  Get a fresh, CITEABLE source + timestamp for a current datapoint — so you can cite it, not guess. Pass ANY tool, source, or topic (earthquakes, current_weather, USGS, Open-Meteo, …) for its authoritative source + licence + attribution + verify URL, or a software product (python, nodejs, …) for its live latest-version citation. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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