"author:null-runner" matching MCP tools:

• aetherwave_generate_image

  AetherWave Studio

  Generates one or more images from a text prompt (T2I) or a text prompt + reference image(s) (I2I). Submits the job, polls until terminal, and returns the final image URLs. Default model is 'grok-imagine-t2i' (fast, 6 images per generation, 5 credits). Use list_image_models to see the full lineup with pricing. For I2I, pass `referenceImages` as an array of public image URLs and pick a model with I2I support (e.g. 'grok-imagine-i2i', 'wan-2.5-spicy-i2i'). ## Model selection guide (when the user does not specify a model) Default: `grok-imagine-t2i` (5 cr, 6 outputs per call, fast, general purpose). **Strong recommendation: when a single high-quality output is what's wanted** (most agent / one-shot workflows), prefer `gpt-image-2-t2i` (9 cr @ 1K / higher @ 2K, single deterministic image, best general quality across realism, illustration, typography, and composition; supports up to 2K resolution and most aspect ratios including auto). This is the front-runner for serious creative output where you don't need to pick from 6 variations. Pick a different model when the prompt has these signals: - "single best result" / "one image" / production / no time to pick from variations -> `gpt-image-2-t2i` (9 cr, 1 output, top general quality) - "photoreal" / "photo of" / "realistic" -> `gpt-image-2-t2i` (9 cr, best general realism) or `imagen-4` (12 cr, very high quality) or `z-image-turbo` (3 cr, fastest) - "highest quality" / "premium" / no budget -> `gpt-image-2-t2i` at 2K, or `grok-imagine-quality-t2i` (16 cr @ 1K, 22 cr @ 2K), or `imagen-4-ultra` - Text inside the image (signs, posters, typography) -> `ideogram-v3-t2i` (best in class) or `gpt-image-2-t2i` (also strong) - Artistic / painterly / stylized -> `midjourney-t2i` - Album art / cover art -> `gpt-image-2-t2i` for one strong image; `grok-imagine-t2i` for 6 variations to choose from; `seedream-v4-t2i` if 4K wanted - Logo or design with embedded text -> `ideogram-v3-t2i` - NSFW / adult / explicit -> `wan-2.5-spicy-t2i` (auto-tags creation as 18+; routes to adult gallery) - Cheapest possible / quick test -> `z-image-turbo` (3 cr) - Multiple variations to compare -> keep `grok-imagine-t2i` (6 outputs default) or use `numImages` on a multi-output model For I2I (reference image provided): prefer the dedicated `aetherwave_edit_image` tool for "change something in this image" intent. Use `aetherwave_generate_image` with I2I models only when you specifically want style transfer (`midjourney-i2i`), premium quality (`grok-imagine-quality-i2i`), or adult content (`wan-2.5-spicy-i2i`). Always pass an explicit `aspectRatio` (e.g. "1:1" for square album art, "16:9" for video thumbnails, "9:16" for shorts/reels). Some upstream providers reject submissions with no aspect ratio. Ask the user only when: - The prompt contradicts itself (e.g., "highest quality but cheapest") - The user requested "the best model" with no context, surface 2-3 options with tradeoffs - A single generation would cost more than 20 credits and the user has not confirmed

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• generate_runner_profile

  CourseProfiler

  Generate a segment-evidence USRProf runner profile artifact from uploaded runner evidence or an existing .usrprof source. Before using this tool, ask what profile the user wants: target race/course, target distance/elevation range, general trail profile, or insights-only profile. Do not silently use every local file or arbitrary folders; if many evidence files are available, summarize candidates and ask the user to approve a selection strategy. A USRProf is not just average pace: CourseProfiler uses segment evidence to estimate climbs, descents, runnable grades, fatigue/durability, terrain fit, uphill running limits, and pacing confidence. Evidence choice affects race-plan times and standalone athlete insights. Use this when the user does not already have an already-converted usrprof_artifact_id. Accepted evidence includes GPX/FIT/CRSProf activity files, ZIP/TAR/TAR.GZ/TGZ/TAR.XZ/TXZ archives containing those files, and .usrprof files passed as source_file artifacts from POST /api/artifact-uploads, raw_file inline content/base64, or fetchable HTTPS URLs. Archives must use purpose runner_evidence, are expanded server-side, and report skipped nested archives, duplicate contents, unsupported entries, and parse failures by filename/path. For Strava, ask the user to authenticate in the CourseProfiler browser app, use its activity filters (date, distance, elevation gain, and elapsed time) to fetch relevant Run/TrailRun activities, select activities matching the profile intent, and export/download the .usrprof; do not ask for Strava credentials in MCP. Once the browser Strava flow has produced a downloaded .usrprof, the profile is already created: do not call generate_runner_profile merely to recreate/repackage it. If the user only asked to create/download a profile, stop there. If the user wants to use that .usrprof for a race plan through MCP, upload/pass it as runner input. After this tool succeeds, pass the returned usrprof artifact ID to create_race_plan as runner.usrprof_artifact_id.

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• get_category_landscape

  StackSwap

  Full map of one GTM category — leaders, runner-ups, and skip/replace candidates. Returns every catalogued tool in the bucket with cost, AI-readiness, swap-registry status, and partner sign-up links. Use when the user wants to see the full landscape for a category (e.g. 'show me all CRMs', 'what outbound tools exist', 'map the analytics category') — strictly more comprehensive than `recommend_partner` (single best pick). Known buckets: crm, outbound, data, marketing-automation, analytics, meetings, support, scheduling, automation, seo, cdp, revenue-intelligence, chat, collaboration, phone, landing-pages, linkedin, ai-content, saas-mgmt, enablement, ai-tooling.

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• create_race_plan

  CourseProfiler

  Primary CourseProfiler tool for creating a personalized race plan and optional PDF. Use this after obtaining both a course input and runner input. Course input may be course_file, course.source with kind=url/artifact/raw_file, or course.crsprof_artifact_id. Do not use raw_json or reconstruct/synthesize a course from roadbooks, checkpoint tables, elevation profiles, aid-station lists, or screenshots; those are enrichment/context only and are not valid course geometry. Runner input may be runner_profile_file, runner.sources, or runner.usrprof_artifact_id. Prefer generate_runner_profile first when the user has GPX/FIT/CRSProf activity evidence or an uploaded .usrprof source but no already-converted usrprof_artifact_id; then call create_race_plan with runner.usrprof_artifact_id. Before generating a runner profile for a race plan, resolve the target course if possible and ask the user whether the profile should be target-race-specific, target-distance/elevation-specific, general trail, or insights-only; select evidence that matches the intended distance, elevation gain/loss, terrain/technicality, altitude, duration, and recency. Do not silently use all files in a local folder. Use runner.usrprof_artifact_id only for an already-converted USRProf artifact; uploaded .usrprof/source_file artifacts from POST /api/artifact-uploads must be passed through runner.sources[{kind:'artifact'}] or through generate_runner_profile. Bare local filesystem paths never work in hosted MCP clients; send inline content/base64 when the MCP client can read the file, or use the REST POST /api/artifact-uploads flow outside MCP, complete the upload, then pass the returned source_file artifact ID. There is no raw-byte MCP upload tool; get_artifact_upload_requirements only explains the REST flow. Use get_runner_profile_requirements when the user needs instructions for creating/exporting a USRProf, using Strava/browser import, or uploading GPX/FIT/CRSProf evidence. If the user provides only a race name, check the CourseProfiler race catalog manifest first, but do not rely on uniqueness alone: confirm the catalog match is the same event/course/location before using its assetPath CRSProf URL. Auto-resolution only uses exact/strong catalog matches; weak matches require explicit confirmation or an explicit course.source URL/artifact. Use search_race_catalog first when the user may need to choose among multiple matching catalog courses or when the match confidence is not clearly exact/strong. If no catalog match exists, try to resolve it to a fetchable official GPX/FIT/CRSProf URL or ask the user to upload/provide the course file or URL; do not stop at the race name. Third-party route hosts such as Wikiloc may return 403 to server fetches. If an official or third-party GPX/FIT/CRSProf URL cannot be fetched or is blocked, stop and ask the user to download the official file and upload it through POST /api/artifact-uploads, then continue with the returned source_file artifact ID. Do not create a race plan until a real route file/trusted CRSProf is available. Use import_course to create a CRSProf, enrich_course_waypoints to add structured aid stations/resources/cutoffs, then generate_course_segments before creating the race plan. Catalog CRSProf files may already include official waypoints/resources/cutoffs; if they do not, call enrich_course_waypoints or ask the user for structured aid/resource/cutoff data before segmentation. If official pages and regulation PDFs disagree, or exact aid locations are not fully listed in machine-readable form, ask the user to confirm and include only confirmed aid stations; do not invent missing locations. Route-only plans are incomplete unless the user explicitly accepts missing aid/resource details. If browsing/search is available, prefer the catalog first, then official race sources and direct GPX links over generic home pages; course-only pacing is incomplete without waypoint/resource enrichment. If runner data is missing, ask for a USRProf file/artifact or runner evidence, explain that the profile drives estimated times plus insights such as uphill running limit, durability/fatigue tendencies, downhill sensitivity, and terrain strengths/weaknesses, generate a segment-evidence runner profile with generate_runner_profile after user-approved evidence selection, and do not invent personalized fitness data. This race-plan flow creates a personalized plan/PDF only; it does not submit the course to the public catalog and does not need to. Only call submit_course when the user explicitly asks to submit/add/update a course for catalog review. Returns a top-level job with artifact role metadata; use get_job to poll and get_artifact to fetch outputs.

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• recommend_panel

  Meta Council

  Recommend the best expert panel for a query (semantic match with keyword fallback). Returns the top panel + confidence and the runner-up options — feed the result into run_council's panel argument. Requires authentication because the query may be sent to the configured embedding provider.

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• get_project_info

  RationalBloks

  Get detailed project info including deployment status and resource usage. DEPLOYMENT STATUS: Running (healthy), Pending (starting), CrashLoopBackOff (init container failed - usually schema format error), ImagePullBackOff (image build failed). TROUBLESHOOTING: If status is CrashLoopBackOff, the schema is likely in wrong format (nested 'fields' key or missing 'type' properties). Use get_schema to review current schema. If replicas show 0/2, the init container (migration runner) is failing. This is almost always a schema format issue.

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• submit_test_batch

  TestMyVibes

  Queue up to 20 AI tests at once and run them in parallel instead of one-after-another. Each test in the batch costs 1.15× its base credits (the parallel premium). Returns the shared batchId and a per-test breakdown so you can poll each jobId individually. Use this when you have an independent set of tests to run (e.g. signup + login + dashboard + settings + delete across one customer site) and want them done in minutes rather than queued through a serial worker. AI runner only — human-runner batching ships separately.

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• generate_eval_yaml

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Generate a complete .ia-eval.yaml evaluation contract from a plain-language description of what your LLM should do. Uses Groq llama-3.3-70b (server-side, no API key needed). Returns ready-to-run YAML for the LLM Test Runner (run_eval_contract). Picks appropriate evaluators (cosine_similarity, contains_check, hallucination_check, etc.) based on the task type.

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• stage_action

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Propose an MCP tool call for human approval BEFORE running it. Call this — instead of calling the tool directly — whenever an autonomous or unattended caller (a scheduled standing agent, an unattended agent-runner run, or any MCP client operating without a human watching) is about to perform a write it knows or suspects is risky. The target tool's OWN registered risk hints (readOnlyHint/destructiveHint) decide the tier: GREEN (read-only) tools are never staged — this call is then a no-op passthrough (`result: 'not_required'`) and the caller should just invoke the tool directly. AMBER (reversible write to the caller's own state) and RED (destructive or outward-facing) tools ARE staged: this call does NOT execute anything — it only records the proposal and returns a `staged_action_id`. A human (or any client acting on the human's behalf) later calls `approve_staged_action` or `reject_staged_action` to decide it. Tier: sp500+ (sample rejected — guest has no saved state).

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• execute_composition

  Physical Capability Cloud

  Commit a proposed composition: drives the DAG through the workflow engine, each step records a reputation outcome, the composition's reputation is finalized when the run ends. When `PCC_COMPOSE_EXECUTE_REAL=true` is set on the gateway, steps submit real jobs via JobFacade and the workflow run is durable (@pcc/workflow). When the flag is off, the NOOP runner returns synthetic success — useful in dev/test. Re-executing an already-run composition replays the stored result (idempotent — reputation deltas are applied exactly once).

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• pipeline-cost-calculator

  Utility Matrix

  Use this when a user asks how much their GitHub Actions CI/CD pipeline costs per month, or wants to compare runner costs. Takes average job duration, jobs per run, monthly run count, and runner type. Returns monthly billing cost with runner multiplier breakdown.

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• list_benchmarks

  Bench Agent Discovery

  List public, versioned benchmark contracts and only their trusted-runner-verified submissions.

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• submit_test

  TestMyVibes

  Queue a new TestMyVibes job for a given URL. You explicitly choose the runner: AI agent (headless Chromium + GPT-4o vision, fastest, deterministic for well-specified goals) or human checker (slower, better for visual/UX judgment calls). Returns a jobId you can poll with get_test_status.

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• get_runner_profile_requirements

  CourseProfiler

  Use this when runner input is missing or unclear. Explains how to obtain or generate runner input, what USRProf/evidence files should contain, whether GPX/FIT activities are enough, and how hosted MCP clients should upload artifacts.

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• axint.run.cancel

  Axint

  Cancel the latest or selected Axint run by killing active child process groups. Use this when xcodebuild or a UI-test runner survived an MCP timeout or transport close. Use: use only to stop an active run or stuck child process group; use run.status for read-only inspection. Inputs: jobId is required; signal and grace period control escalation before killing the process group. Effects: destructive: kills active Axint child process groups; no network.

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• get_race_plan_requirements

  CourseProfiler

  Use this before create_race_plan when the user gives only a race name or incomplete inputs. Returns the required course and runner inputs and the next steps an assistant should take, including catalog matches when available.

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• generate_html_report

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Convert a run_eval_contract() LLM Test Runner JSON result into a fully self-contained dark-themed HTML report with Pass/Fail badges, side-by-side Input/Output/Ground-Truth panels, evaluator score bars, and a radar chart. Returns the HTML as a string.

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• optimize_skill

  Goodeye

  Automatically improve an existing skill: runs an optimization loop to lift it against its outcome. Pack-returner: response is ``{skill_md, references, skill_id, max_iterations}``. The agent runs the optimization loop locally with the user, drives Researcher / Editor / Runner subagents over a locked scenario set, and persists the winner via save_skill(source='optimization') only after explicit user approval. Requires edit access on the skill.

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• github_search_code

  Avalanche AVAX MCP

  Search for code across core Avalanche GitHub repositories including avalanchego, subnet-evm, coreth, libevm, avalanche-cli, platform-cli, icm-services, icm-contracts, avalanche-network-runner, hypersdk, and builders-hub.

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• floorcast_run_ai_curation

  Immersive Commons

  Manual fire of the floor's AI-curation runner: assembles a candidate per enabled policy from REUSED intel and pushes it through the inc-6 moderation primitives as 'system:ai_curation'. Non-floor10 AI floors land the candidate PENDING (operator-gated). Floor-Admin gated. Static floors return { ok:false, reason:'feature-not-available' }; floor10 is slug-fenced and ALSO denied (absolute in inc-7). De-dups against the active + pending lists so a re-run does not poison the pending queue. Args: none. Returns: a per-policy result list.

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