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768,476 tools. Updated 2026-11-12 04:01

"Resources or information on analysis techniques or methods" matching MCP tools:

  • Search the MITRE D3FEND catalog of defensive techniques by keyword, tactic, or targeted artifact. Default response is SLIM (drops `uri` from each row — saves ~60 chars/row, ~30% on popular drills); pass include='full' for the verbose record. Pass exclude_id when chaining from d3fend_defense_lookup to skip self in sibling-artifact searches. Use to discover defenses applicable to a given threat model — e.g. 'what defenses harden access tokens?' (tactic=Harden + artifact='Access Token'). Drill into d3fend_defense_lookup with any returned defense_id for the ATT&CK technique mappings. Free: 30/hr, Pro: 500/hr. Returns {query, total, results [{defense_id, label, uri (only when include=full), parent_label, tactic, artifact}], next_calls}.
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  • 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. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. 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 (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
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  • Generate cinematic video from a text prompt. Uses MiniMax H3 — top 2 on the Artificial Analysis text-to-video leaderboard — with native audio. Async — returns requestId, poll with check_job_status. 768p or 2K (2K is upscaled from a 768p render), 5-15 seconds, priced per second by resolution (native audio free). Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_video' and duration, resolution params.
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  • Fetch the full content for one id returned by `search`: a rental listing (rental:<uuid>), a plan set (plan:<uuid>) or a published page (page:<route>). Returns {id, title, text, url, metadata}. Always cite the url. Hosts that can call specific tools should prefer get_rental or the resources.
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  • Search the MITRE ATLAS catalog of AI/ML attack techniques by keyword, tactic, or maturity. Default response is SLIM (description truncated to 240 chars per row); pass include='full' for the verbose record. Pass exclude_id when chaining from atlas_technique_lookup to skip self in sibling-tactic searches. Use this to discover techniques matching a threat-model question, e.g. 'what techniques target LLM serving infrastructure?'. Drill into atlas_technique_lookup with any returned technique_id for the full description, ATT&CK bridge, and pivot hints. For broader cross-referencing: when a result has attack_reference_id, that bridges to D3FEND mitigations via d3fend_defense_for_attack. Free: 30/hr, Pro: 500/hr. Returns {query (echoed filters), total, results [{technique_id, name, description (truncated by default), tactics, inherited_tactics, maturity, attack_reference_id, subtechnique_of}], next_calls}.
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  • Long-poll: blocks until the next edit lands on this board, then returns. WHEN TO CALL THIS: if your MCP client does NOT surface `notifications/resources/updated` events from `resources/subscribe` back to the model (most chat clients do not — they receive the SSE event but don't inject it into your context), this tool is how you 'wait for the human' inside a single turn. Typical flow: you draw / write what you were asked to, then instead of ending your turn you call `wait_for_update(board_id)`. When the human adds, moves, or erases something, the call returns and you refresh with `get_preview` / `get_board` and continue the collaboration. Great for turn-based interactions (games like tic-tac-toe, brainstorming where you respond to each sticky the user drops, sketch-and-feedback loops, etc.). If your client DOES deliver resource notifications natively, prefer `resources/subscribe` — it's cheaper and has no timeout ceiling. BEHAVIOUR: resolves ~3 s after the edit burst settles (same debounce as the push notifications — this is intentional so drags and long strokes collapse into one wake-up). Returns `{ updated: true, timedOut: false }` on a real edit, or `{ updated: false, timedOut: true }` if nothing happened within `timeout_ms`. On timeout, just call it again to keep waiting; chaining calls is cheap. `timeout_ms` is clamped to [1000, 55000]; default 25000 (leaves headroom under typical 60 s proxy timeouts).
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.
    33 npm
    1
    MIT
  • F
    license
    Not graded
    quality
    C
    maintenance
    MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.
    -

Matching MCP Connectors

  • Oregon DMV MCP — live wait times at all 60 Oregon DMV field offices, plus the office locator the…

  • Crypto scam checks (tokens, sites, wallets), chain reads, web page to Markdown, email checks.

  • Run a read-only SQL SELECT against water data tables staged on a DataCanvas by water_get_series or water_find_sites. Workflow: run water_get_series or water_find_sites (get canvas_id + table_name) → water_dataframe_describe (confirm the table and its columns) → water_dataframe_query (SQL analysis), then optionally water_dataframe_drop (when this server enables it) to remove a table you are done with. Only SELECT statements are permitted. At most 10,000 rows are returned; a query matching more is capped and the response sets truncated=true — scope with WHERE/LIMIT, and use SELECT COUNT(*) or water_dataframe_describe to learn the true match count. Requires DataCanvas to be enabled on this server instance. Returns an error if DataCanvas is not available.
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  • Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md
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  • Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md
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  • Get a single V³ event: headline, why-it-matters, scores, and how much structured analysis V³ holds on it (perspectives, decisions, outcomes). Use after v3_search_events (or v3_breaking_now) when the user wants detail on a specific event. This returns V³'s public summary; the full country-by-country perspectives, timeline, decisions to watch, and assessed outcomes are on v3.news at the returned url (a V³ account unlocks the multi-perspective analysis). Args: event_id: the V³ event id from a prior tool result.
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  • READ-ONLY: returns generated source code as text and writes nothing to disk, creates no project and runs no command. Generates an idiomatic @imqueue/rpc service (an IMQService subclass with @expose()d, JSDoc-typed methods) plus a bootstrap that starts it. Provide the methods you want, or omit them for a starter template. Any non-primitive parameter or return type also gets a types.ts with the required @classType()/@property() declarations — without those the generated client types it `any`, which compiles. Use create_service (local install only) if you want files actually written.
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  • Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.
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  • Fetch a single post by id: views, likes, comments, engagement rate, outlier scores for seven time windows, thumbnail and the owning profile. When a transcript or visual analysis already exists it is included at no extra cost. The visual analysis is a structured scene-by-scene breakdown (per-scene timing, on-screen text, visual elements and a recreation note) plus an overall-style summary. Request new enrichment via request_transcript (speech / on-screen text) or request_visual_analysis (scene breakdown). Use after search_outliers to deep-dive a result. Cost: 1 credit per call.
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  • Retrieve the complete markdown of one documentation article by the id returned from `search` (for example `en/claude-code/advanced-techniques/hooks-automation`). The text is returned in full; `metadata.gated` only reports whether the article sits behind the paywall on the web. An unknown id is an error — call `search` first.
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  • Depreciate an asset by straight line or diminishing value, with a year by year schedule. Use for: 'how is depreciation calculated', 'straight line vs diminishing value', 'depreciation schedule', 'book value after 3 years', 'work out depreciation on equipment'. THE TWO METHODS DIVERGE SHARPLY ON THE SAME ASSET. Straight line spreads the deduction evenly across the useful life. Diminishing value applies the rate to the REDUCING book value, so it front-loads the deduction heavily and never reaches zero on its own. Which is better depends entirely on when the owner wants the deduction, so the year by year schedule is the useful part of this answer rather than the first year figure. Show the schedule. additional_costs are capitalised into the cost base, which is where delivery and installation belong. salvage_value acts as a floor under both methods, and the last year of a straight line life takes only what is left, so rounding cannot push the book value below it. ONE IMPORTANT LIMIT, AND IT MATTERS. This is the ARITHMETIC of the two methods on a rate you supply. It is NOT a lookup of Inland Revenue's prescribed rates, which vary by asset class and are published in the IRD depreciation rate finder. If someone needs to know the right RATE for a particular asset, send them to that finder. DO NOT INVENT A RATE to feed this tool and present the output as their tax position. Args: initial_cost: Purchase price of the asset. method: straight_line or diminishing_value. additional_costs: Costs capitalised in, such as delivery or installation. salvage_value: Expected residual value. A floor under both methods. useful_life_years: Years of life. Required for straight line. annual_rate_pct: Rate per year. Required for diminishing value. years_to_schedule: Years to show. Defaults to the life, or ten. Returns: The cost base, the depreciable amount, the first year charge, the total charged, the closing book value and the full year by year schedule.
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  • Return the connected organization, the credential in use, and the granted workspace + environment ("TEST" or "PRODUCTION") targets — exactly the values the required workspaceId/environment arguments on targeted tools accept. Always available; call this first, and again whenever a tool returns UNAUTHORIZED or NOT_FOUND unexpectedly, or after this connection's access changes — resources live per-workspace.
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  • Look up customer-facing Wellness Project product knowledge. Use it for app navigation, visible features, integrations, metric meanings, logging methods, permissions/sync questions, or troubleshooting. Wellness Project-specific answers are closed-book: answer them from this tool's result, never from memory. The result states its own grounding rules. For how-to questions, select the relevant feature topic; a matching published video link is included when one exists. For a request to find, report through, or open the feedback form, use topic=troubleshooting. In-app chat must offer the open_feedback action chip, not print the link (chat links render as plain text). External MCP clients may provide the exact Share feedback link returned. Never claim the form was opened without the user tapping the action. For recognized exercise names or whether an exercise is in the app's exercise library, use list_exercises rather than guessing or expecting the app guide to enumerate exercises. For cost, price, Free vs Pro, Founding Member, upgrading, or the 3-analysis limit, use topic=pricing. Reproduce exactly one returned pricing message verbatim, including its subscription link when it has one, and add no other pricing detail. The app attaches its own upgrade button to that message when one applies; never type out a button, chip, link markup, or call-to-action of your own.
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  • The user's live market board. Start here when they ask how the markets are doing in general: "how are the markets today?", "what's moving?", "market update", a morning check before the open, or "open the market dashboard". One call covers the overview: the user's own symbols on top (their watchlist, then what they asked about most in the last 30 days), then major indices (US, India, Türkiye, Indonesia, VIX), crypto, commodities and FX, each with today's change. Don't also call market_snapshot for the same overview; add other tools only for what the board doesn't show (futures, news, sectors). For a single symbol's price or analysis, use that symbol's own tool instead. Opens as an interactive dashboard; clicking a row asks for that market's chart or analysis. Clients without app support can read the `summary` field.
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  • Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, novel research hypotheses, and research resources (code repositories, datasets, models, and demos linked to papers). For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results. search_type accepts all, cluster, paper, author, institution, venue, hypothesis, or resource. Use paper for literature and get_paper to read a result. Use other sources for biographies, news, and non-research web content. Resources: search_type="resource" searches GitHub repositories, Hugging Face datasets/models, and Zenodo records that Lacuna has linked to at least one paper. To find benchmark or evaluation datasets, use search_type="resource" with resource_kind="dataset" (for example query "image classification benchmark" or a dataset name like "ImageNet"). resource_kind accepts codebase, dataset, model, or demo (one value or a list); provider accepts github, huggingface, or zenodo. Both filters require search_type "resource" or "all" (with either filter set, only resources are returned). Each result has an external `url`, a Lacuna `context_url`, and `linked_paper_count`; call get_resource on its id to list the linked papers (with their relationship to the resource), then get_paper on those papers for reported results and numbers. Resource search does not support date_from/date_to, venue, year sorting, or semantic ranking. ranking_profile accepts: - default / lexical (default): production ranking; relevance-sorted paper searches combine lexical and semantic retrieval when fields is unset. - semantic: conceptual paper retrieval; supported for paper and all. - bm25_title_abstract / bm25: lexical paper matching over those fields. sort accepts relevance (default), year_desc, or year_asc. Semantic ranking cannot use year sorting; constrain recency with date_from/date_to instead. date_from and date_to are inclusive YYYY, YYYY-MM, or YYYY-MM-DD bounds. author_id_or_url constrains a paper search to one author. Pass an author ID or Lacuna author page URL; it requires search_type="paper". fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue, plus description, topics, provider_key, and paper_titles for resources (a resource's title already includes its description, topics, README text, and linked paper titles). Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.
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  • Royalty analysis: benchmark royalty-rate ranges by IP type and industry, adjust a base rate for deal factors, and apply the 25% rule of thumb. Method selects the formula. Use to select and support a royalty rate before a relief-from-royalty or royalty-capitalization valuation. To apply the chosen rate in a valuation use valuation_income_methods (relief_from_royalty) or valuation_market_approach (royalty_capitalization); for transfer-pricing pricing use valuation_compliance. Per method: benchmark needs ip_type + industry (optional: comparable_database); adjust needs base_royalty_rate + adjustment_factors; twenty_five_percent_rule needs licensee_expected_profit (optional: profit_attribution_to_ip). adjustment_factors multiply the base rate, so values above 1.0 raise the rate. Only method is required; all other parameters are method-dependent — supply those the selected method names and omit the rest (defaults apply where defined). Rates and premiums are decimals (0.10 = 10%). Pure arithmetic: no I/O and no external calls, rounded to 2 decimals; parameters belonging to other methods are accepted and ignored. An unknown method, or a missing method-required parameter, returns an error instead of a value.
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  • List the API keys and client-credential pairs of an organization. For each key: its ID, name, type, creation, expiry, last use, revocation time, and the resources it can access. For each client-credential pair (an OAuth client that authenticates with the client credentials grant): its client ID, name, creation time, creator, grant types, scopes, and the resources it can access. Key values and client secrets are never returned. Pass a key's ID to RenameApiKey or DeleteApiKey. Those tools do not work on pairs, so manage pairs in GraphOS Studio or with `rover api-key`. Provide the organization ID, which GetMyIdentity returns. Optionally filter keys by status (ACTIVE by default, or ALL, EXPIRED, REVOKED) and type (OPERATOR, SUBGRAPH, VARIANT, GATEWAY, SCIM). The filters do not apply to pairs. The two lists page separately: page keys with first and after, and pairs with pairsFirst and pairsAfter.
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