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602,394 tools. Updated 2026-09-23 08:42

"Techniques for improving prompt effectiveness" matching MCP tools:

  • Return canonical synthesis / patching techniques with role-keyed module realizations drawn from the corpus. Use this when the user asks "how do I do X?" with X being a recognisable technique (low-pass-gate plucks, pinged-filter percussion, parallel multiband processing, complex-oscillator FM, karplus-strong pluck, clocked-delay feedback, modal-resonator excitation, wavefolder harmonics, envelope-follower ducking, Maths-style function-generator omnibus). It's also the right tool when the user has a module and asks "what's this good for?" — pass filter.module_id to retrieve every technique that references the module via its role_realizations. Each technique declares role_definitions (the roles the technique uses, each with required and optional affordances) and role_realizations (concrete modules that fill each role, with the affordances they provide). The model substitutes modules from the user's rack into roles by affordance match — DO NOT treat the realization list as exhaustive or as a recipe. Args: - filter (optional): { capability?, module_id?, text? } - capability: kebab-case capability id (see search_modules _meta.taxonomy). Returns techniques whose required *or* optional capability list includes this id. - module_id: "<manufacturer>/<module-slug>". Returns techniques that have a role_realization referencing this module. - text: free-text phrase. Substring-matches against technique id/label/description AND a curated alias table (technique_aliases) — that's the right surface when a user types evocative prose like "stuttering delay", "plucked string", "source of uncertainty" that doesn't grep against any kebab-case id. Two-way alias match: long alias ("source of uncertainty") matches short query ("uncertainty"), and vice versa. - When multiple filters supplied, AND-intersects. - Omit filter entirely to list all techniques. Returns: { "techniques": [ { "id": "low-pass-gate-pluck", "label": "Low-Pass Gate Pluck", "description": "Send a short envelope...", "required_capabilities": ["lowpass-gate"], "optional_capabilities": ["envelope-generator", "function-generator"], "role_definitions": [ { "role_id": "lpg", "description": "The vactrol-based or vactrol-emulating element. Strictly required...", "required_affordances": ["lowpass-gate"], "optional_affordances": [] }, ... ], "role_realizations": [ { "role_id": "lpg", "module_id": "make-noise/optomix", "affordances_provided": ["lowpass-gate"], "notes": "Two-channel vactrol-based LPG..." }, ... ], "canonical_instance": { "rationale": "...", "lineage": [ { "position": 1, "label": "Buchla 292 (1970)", "module_id": null, "notes": "..." }, { "position": 2, "label": "Tiptop Audio Buchla 292t", "module_id": "tiptop-audio/buchla-292t" }, ... ] }, "counter_canonical_notes": [ { "claim_pushed_back_against": "Optomix is the canonical pairing with Plaits...", "evidence": "The corpus catalogs 19 LPG-capable modules..." } ], "coverage": [ { "role_id": "voice", "realizations_count": 3 }, { "role_id": "lpg", "realizations_count": 19 }, { "role_id": "env", "realizations_count": 6 }, { "role_id": "clock", "realizations_count": 2 } ] } ], "_meta": { "filter": {...}, "feedback_hint"?: string } } How to use role data: - role_realizations are CURATORIAL SAMPLES, not exhaustive lists. The coverage[].realizations_count tells you how many are documented; other modules may fill the same role. - To find modules in the user's rack that can fill a role, use find_role_realizations(technique_id, role_id, available_modules). - canonical_instance is opt-in and sparse. Most techniques don't have one; that absence is information. When present, it documents a documented historical lineage (e.g., Buchla 292 → 292t → MMG → Optomix for low-pass-gate-pluck) — NOT a prescription. - counter_canonical_notes push back on likely training-data priors. When the user invokes a canonical-sounding claim that has a counter_canonical_note, surface the pushback. Errors: - "Module not found: <id>" if filter.module_id is supplied and unknown. - Empty techniques[] with a feedback_hint when filters produce no matches — call report_gap if the user expected coverage.
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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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  • List the full eDiscovery Decoder MCP surface — every tool, prompt, and resource, plus the suggested demo flow and safety boundaries — with an example prompt for each. Call this first when you are unsure which tool fits the user's question, or when tool-search shows only a partial list.
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  • Ask the KaiCalls on-behalf update broker to perform a scoped, governed mutation. Supported intents: phone.emergency_address.set, transcripts.sink.configure, agent.patch, numbers.purchase, agent.config.rollback. Prefer update_agent_config for prompt/voice/model edits, configure_agent_business_rules for a single named operational prompt section, upsert_lead for lead create/update, send_sms for outbound texts, configure_staff_alerts for alert recipients/rules, and configure_textable_links for send-link setup. Mutating requests need an idempotency_key; high-risk changes need human authority. The broker returns needs_user_input, needs_approval, pending_approval, executed, denied, or unsupported — never an unaudited side effect.
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  • Activate or deactivate a prompt by its ID. Use is_active=true to restore a previous prompt version — the currently active prompt of the same type and subtype is deactivated automatically. To change prompt text, use create_prompt instead.
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  • Activate or deactivate a prompt by its ID. Use is_active=true to restore a previous prompt version — the currently active prompt of the same type and subtype is deactivated automatically. To change prompt text, use create_prompt instead.
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Matching MCP Servers

  • A
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    Enables AI models to interactively prompt users for input or clarification directly through their code editor. It facilitates real-time communication between assistants and users during development tasks.
    MIT
  • A
    license
    A
    quality
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    maintenance
    Optimizes prompts using meta-prompting techniques through a two-turn approach that first provides optimization guidelines and then refines the prompt for enhanced quality and effectiveness.
    1
    3 npm
    4
    MIT

Matching MCP Connectors

  • Answers: after a change was made, did this store's revenue signals actually recover? Compares the window before a stated change against the window since: paid vs pending vs failed order mix, webhook failure counts, and gateway availability. Returns `resolved`, `improving`, `still_failing`, or `unknown` — and `unknown` when too little new data has arrived to tell, which is free. A quiet ten minutes is not a recovery. Free while in beta.
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  • Answers: after a change was made, did this store's revenue signals actually recover? Compares the window before a stated change against the window since: paid vs pending vs failed order mix, webhook failure counts, and gateway availability. Returns `resolved`, `improving`, `still_failing`, or `unknown` — and `unknown` when too little new data has arrived to tell, which is free. A quiet ten minutes is not a recovery. Free while in beta.
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  • Fiche PIM d’un produit : descriptions, points clés, questions fréquentes, contraintes de fichier (fond perdu, résolution, marges, mode colorimétrique) et ressources (visuels, gabarits, consignes techniques). À utiliser pour DÉCRIRE un produit ou aider à préparer un fichier. Ne renvoie aucun prix — pour coter, passez par quote_product. Fournissez exactement un argument. Les champs absents ne sont pas renseignés au PIM ; la FAQ est rare.
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  • Start generating hero image variants for an idea's ad. Runs in the background. Args: - ideaId (string) - prompt (string, optional): defaults to the idea's saved image prompt Returns: { job: { id, status, prompt, variantCount, results }, note }. Poll idealaunch_get_image_job until status is 'succeeded', then choose one with idealaunch_apply_hero_image. Consumes one of the idea's AI generation turns. Costs no Ad Run credit.
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  • Search detailed documentation for Strudel live coding or ABC/ABCJS notation. Returns relevant code examples and explanations from the official docs. Use this when the curated guides (get-strudel-guide, get-music-guide) don't cover what you need — for specific functions, advanced techniques, or when you're unsure about syntax. Powered by semantic search over strudel.cc and ABCJS docs.
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  • Input: A muted video URL along with a textual prompt describing the desired audio. Output: We will return the video URL with the applied audio. Functionality: This tool now takes a muted video and a text prompt as input. It generates an audio track based on the provided prompt and applies this audio to the video, resulting in a video with integrated sound. Steps: 1. We will get the user_id from the request context. 2. We will validate the user's generation tokens. 3. We will call the Audio Application API with the muted video URL and the provided prompt. 4. The API will generate the audio from the prompt and merge it with the muted video, returning a JSON response with the updated video URL. 5. We will return the updated video URL to the user. INSTRUCTION FOR CLIENT MODEL: - Extract the required input parameters 'video_url' (type: string, URL) and 'prompt' (type: string, describing the desired audio) from the user's prompt. - Ignore any extraneous information in the user's input. - Pass the extracted values to this tool as 'video_url' and 'prompt'. - Example: For user input "Add dramatic orchestral music to this video https://example.com/video.mp4", extract 'video_url' as 'https://example.com/video.mp4' and 'prompt' as 'dramatic orchestral music'.
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  • Bulk ATLAS technique lookup — retrieve full records for up to 50 techniques in a single request instead of N separate atlas_technique_lookup calls. Designed as the natural follow-up to atlas_case_study_lookup, whose techniques_used array can be passed directly. Each item is the same shape as atlas_technique_lookup, including parent-tactics inheritance for sub-techniques (inherited_tactics=true flag) and per-item next_calls (D3FEND bridge when attack_reference_id present, sibling-technique search by tactic, parent lookup for sub-techniques). Free: 30/hr (1 per item), Pro: 500/hr. Returns {results [{technique_id, status (ok|not_found|invalid_format), technique, error}], total, successful, failed, partial, summary}.
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  • Generate a single image from a text prompt through Frenchie. Required: prompt. Optional: style (free-text style direction), size, quality, format, background. stdio mode auto-saves the image to .frenchie/<slug>/generated.<ext>; HTTP mode returns a presigned imageUrl that the agent should download for the user.
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  • Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.
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  • Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.
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  • Fetch SnowSure-unique ML/AI trend datasets from the public REST API. Use for powder-day leaders, bluebird-day leaders, bluebird predictions, improving/stable/declining score pulse, per-model accuracy weights, daily SnowSure score component history, ML extended outlook (days 8–14), global forecast trust, and powder/bluebird event logs. Start with dataset=catalog. Its leaderboards read CURRENT-season counters and are global — they take no season and no country/state filter. For a past season, or for any ranking scoped to a state, province, country or region ("most snow days in Maine last season", "rank BC resorts by season snowfall"), use get_season_leaderboard instead. Prefer get_insights for narrative intelligence cards; use this for raw rankings and time series.
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  • Describe what's going wrong — your human's complaint, or a failure you notice in your own behavior — and get the matching techniques. Deterministic matching; if the description fits two problems it returns one clarifying question instead of guessing.
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  • Return datasets whose published freshness trend is recovering, with the fastest staleness reductions first. Includes pipeline-computed trend and publish-reliability evidence. Use it for improving freshness trends; do not use it for deterioration, anomalies, reliability grades, or structural drift—use find_deteriorating, find_anomalies, find_unreliable, or find_schema_drift instead. It reads precomputed trend data, so an empty result means no published recovering row exists; DataPulse is read-only, requires no API key, and the edge limits clients to roughly one request per second with a small burst, so pace or retry.
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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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  • Text generation against the writing-model catalog (Claude, Gemini, GPT, Llama, DeepSeek…) — ad copy, hooks, scripts, rewrites, brainstorms. Prompt-only, no ad assembly (for a finished on-brand creative use plan_ad -> render_ad). BY DEFAULT the model answers as a marketing copywriter (a short house system prompt is applied, which is what you want for ad copy); pass raw:true for a plain, unstyled answer from the model itself with NO system prompt at all. model = a writing-model id from hermoso_capabilities (omit for the default Claude orchestrator). Paid (a credit or two by length).
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