Inference, embeddings and prompt execution. Every tool below was read from that server's own
tools/list, so the name and arguments are what the server exposes, not what
its description claims.
| Tool | Server |
|---|---|
| llm-proxyanswers PAID MCP TOOL — $0.021 USDC per successful call via native x402. Discovery is free. LLM inference proxy — pay USDC, get AI responses without managing API keys. Accepts a prompt and optional system instruction, forward... arguments: prompt, system, model, max_tokens | The Stall ai.intuitek.the-stall |
| estimate_llm_costanswers Estimate the cost of an LLM/API workload (input + output tokens) for a usage-priced provider (OpenAI, Anthropic, AWS Bedrock…) from its real rate card. arguments: slug, model, inputTokens, outputTokens | CostBench com.costbench |
| read_llm_discoveryanswers Read-only fetch of an IntoDNS.ai LLM/agent discovery file: llms.txt (canonical agent index), llms-full.txt (full prompt-ready context), llms.json (structured prompt routing), llm/api.md (Markdown API guide), openapi.j... arguments: file | IntoDNS.ai DNS & Email Security Scanner ai.intodns |
| llmanswers Ask a DIFFERENT LLM a question and get its answer, billed per token from the Vaaya wallet (model cost + 3%, usually a fraction of a cent). Use it to get a second opinion from a rival model, cross-check an answer, summ... arguments: prompt, model, system, max_tokens | Vaaya ai.vaaya |
| detect_prompt_injectionanswers Classify a prompt before it reaches your LLM. Brainiall Prompt Shield engine.
Returns category (jailbreak | prompt_injection | data_exfiltration | impersonation | none),
severity, reason, confidence. arguments: prompt | Brainiall NLP com.brainiall |
| prompt_caching_savings_calculatoranswers Prompt Caching Savings Calculator (LLM API Costs) — Estimate how much prompt caching cuts your LLM API bill: monthly input tokens, cacheable share, and hit rate give per-model savings, cache writes included. arguments: monthlyInputTokensM, cacheableSharePct, hitRatePct, modelId | com.calcfleet/calculators com.calcfleet |
| promptfoo_importanswers Import a promptfooconfig.yaml. Creates a prompt, a dataset from the test vars, and metrics from the assert blocks (llm-rubric/g-eval become judge metrics; contains/equals/regex/is-json become deterministic check metri... arguments: config | CompletionKit com.completionkit |
| update_segment_promptsanswers Set one segment's final prompts VERBATIM — no LLM rewrite. The direct
counterpart to update_segment_content: your text is written as-is to the
segment's creative direction and to the matching asset configs the... arguments: project_id, segment_number, image_prompt, start_frame_prompt, video_prompt, media_queries | framesail com.framesail |
| agentllm_micro OpenAI-compatible AgentLLM Micro text inference for classification, extraction, routing and short summaries. Run prepare_agentllm_micro with the identical arguments first. Hard limits: 2,400 UTF-8 input bytes, 8 messa... arguments: prompt, system, max_tokens | EU Compliance Tools (pay-per-call, x402) io.github.patrickpi1312 |
| chat_completionanswers Send a conversation to any text model available through CCAPI (Claude, GPT, Gemini, DeepSeek, GLM, MiniMax, Kimi, Qwen…) and get the reply. Useful for consulting a second model for a different perspective, running a c... arguments: model, messages, max_tokens, temperature, top_p, stop | CCAPI ai.ccapi |
| search_promptsanswers Search the PromptSharp prompt library. Returns ready-to-run AI prompts scoped to your license tier (no token = 15 public teasers; vertical Pro = your vertical; all-access = the full cross-vertical library). Locked res... arguments: query, vertical, section, limit | ai.promptsharp/promptsharp ai.promptsharp |
| get_promptanswers Fetch one prompt by id: the full copy-paste prompt plus its guardrails and a why-it-works note. Requires that the prompt is within your license scope. arguments: id | ai.promptsharp/promptsharp ai.promptsharp |
| promptarch_lint_artifactanswers Lint an AI agent context file (CLAUDE.md, AGENTS.md, Cursor rules, Copilot instructions, memory files) with PromptArch's deterministic linter: ~30 research-backed rule families covering current-model anti-patterns (to... arguments: content, format, filename | ai.promptarch/mcp ai.promptarch |
| promptarch_list_artifact_typesanswers List the artifact types promptarch_generate_artifact can produce from a project description. | ai.promptarch/mcp ai.promptarch |
| promptarch_generate_artifactanswers Generate an AI agent context artifact (e.g. context_pack, claude_md) from a project description. Requires a PromptArch API key configured as an Authorization: Bearer pk_... header on this MCP server. Consumes credits. arguments: artifact, project_name, project_description, tech_stack, repo_structure, commands | ai.promptarch/mcp ai.promptarch |
| video_to_promptanswers Turn one of your finished Video Analysis reports into ONE reusable generation prompt that recreates the source video's look, energy, pacing and mood, with a {your photo} placeholder where your own subject goes. Pass r... arguments: report_id, video_url, mode, engine | ai.switchapp/switch ai.switchapp |
| thinkneo_optimize_promptanswers Analyze prompt and suggest optimizations with live metrics context. arguments: prompt | ThinkNEO Control Plane ai.thinkneo |
| get_analyst_configanswers Returns the TunnelMind analyst config bundle. Configures any LLM
(Claude, GPT, Gemini, local) to behave as a TunnelMind analyst that
knows the data graph, follows the 5-call golden path, and surfaces
attestation_tier ... arguments: surface, version, receipt | TunnelMind Data API ai.tunnelmind |
| generate_llms_txtanswers Generate llms.txt and llms-full.txt for a site, following the llmstxt.org convention. Inventories the site from its robots.txt, sitemaps, and homepage links, then writes an index file and a full file with page content... arguments: url, refresh | Superflow Free Tools ai.usesuperflow |
| build_avatar_promptanswers Build a highly detailed prompt for generating a unique reusable avatar/model. Use this when the user wants help describing a person before image generation. If the user's prompt is already intentional and specific, Uw... arguments: user_text, model_slug, camera, camera_label, aspect_ratio | Uwear ai.uwear |
| llm_modelsanswers List available text-generation LLM models with per-token pricing and max context. 텍스트 생성 모델 카탈로그를 반환합니다. 각 모델의 1M 토큰당 input/output 단가(포인트), 계열·크기·멀티모달 여부·태그·추천 용도(use_cases)·max_context 를 한 응답에 포함합니다. llm_chat Tool의 m... arguments: family, tag, use_case, multimodal | APICK AI app.apick |
| llm_chatanswers Send a chat request to a selected LLM model and receive the assistant reply. 선택한 LLM 모델에 대화를 보내고 assistant 응답을 받습니다. 서버는 대화 히스토리를 보관하지 않는 stateless 방식 — 매 호출마다 전체 히스토리를 messages 로 전송하고, 응답의 compacted_messages 를 다음 턴의 ... arguments: model, messages, content, system, compact, temperature | APICK AI app.apick |
| send_script_to_teleprompteranswers Turn one or more finished video scripts into a single one-tap link that opens the user's Daily Studio app with the script(s) loaded into the teleprompter and the right platform safe-zones selected, ready to record. Fo... arguments: text, title, wpm, platform, scripts | Daily Studio Teleprompter app.dailystudio |
| get_battle_promptanswers Reveal the challenge after register_for_battle. Returns prompt, rules, output_format, max_spend_usd, time_limit_minutes, prompt_revealed_at, and deadline_at; reading it starts your deadline clock. Answer locally, then... arguments: battle_id, registration_id | Agent Coliseum MCP app.nanocorp.agentarena |
| get_suggested_promptsanswers Onboarding suggestion chips for the chat surface — the same 'What's trending?' / 'Find me a gift' / 'Compare products' chips chat.curie.app shows above its input. When called on a per-shop MCP endpoint (e.g. /api/mcp/... arguments: count, category | Curie Commerce co.curie |
| request_job_completionanswers Prepare a transaction to submit job completion as the assigned agent. Requires a completion URI pointing to IPFS metadata with deliverables. arguments: jobId, completionURI | AGI Alpha com.agialpha |
| architect.validateanswers Pro/Teams — first-pass doctrine review of agentic code/workflow against the 10-principle Agentic AI Blueprint. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running LLM call (60-180s typical); MCP clients commonly ... arguments: implementation_context, focus_area, task, language, repository, files | AI Design Blueprint com.aidesignblueprint |
| get_fastest_completion_pathanswers Find the fastest official way to complete a practical task. Use for requests like “how do I cancel,” “return an item,” “get a refund,” “file a warranty claim,” or “find the official form.” It returns ordered, source-b... arguments: provider, task, state | taskrail com.alamavar.taskrail |
| llm_api_cost_calculatoranswers LLM API Cost Calculator (GPT-4o, Claude, Gemini) — Estimate monthly API costs for GPT-4o, Claude, and Gemini from tokens per request and volume. See input vs output cost split. Prices as of 2025 — verify. arguments: model, inputTokensPerReq, outputTokensPerReq, requestsPerMonth | com.calcfleet/calculators com.calcfleet |
| llm_self_host_vs_api_calculatoranswers LLM Self-Host vs API Cost Calculator — Find the monthly token volume where self-hosting an LLM on rented GPUs beats paying per token for an API. Compare costs, GPUs needed, and breakeven point. arguments: monthlyInputTokensM, monthlyOutputTokensM, apiModelId, gpuId, gpuHourlyUsd, throughputTokensPerSec | com.calcfleet/calculators com.calcfleet |
| llm_throughput_calculatoranswers LLM Throughput & GPU Sizing Calculator — Estimate how many GPUs your LLM needs: concurrent users and target tokens per second become cluster size, monthly cloud cost, and real utilization at load. arguments: concurrentUsers, targetTokensPerSecPerUser, gpuId, gpuHourlyUsd, aggregateTokensPerSecPerGpu, utilizationHeadroomPct | com.calcfleet/calculators com.calcfleet |
| claidex_research_promptanswers Compose a rigorous, reusable investigation prompt that tells an MCP client which Claidex tools and resources to use. arguments: objective, target_gene, disease, risk_tolerance | Claidex MCP com.claidex |
| rank_documents_by_embeddinganswers Embed a query and candidate documents, then rank documents by cosine similarity. Use for semantic matching, retrieval checks, clustering triage, and lightweight RAG over user-provided passages. arguments: query, documents, top_k | Claidex MCP com.claidex |
| prompts_listanswers List all prompts | CompletionKit com.completionkit |
| prompts_getanswers Get a prompt by ID arguments: id | CompletionKit com.completionkit |
| prompts_createanswers Create a prompt arguments: name, description, template, llm_model, tag_names | CompletionKit com.completionkit |
| prompts_updateanswers Update a prompt. If the prompt already has runs, this creates a new DRAFT version (current=false) rather than editing in place or publishing — promote it with prompts_publish — so an agent's edits don't go live withou... arguments: id, name, description, template, llm_model, tag_names | CompletionKit com.completionkit |
| prompts_deleteanswers Delete a prompt arguments: id | CompletionKit com.completionkit |
| prompts_publishanswers Publish a prompt version, making it the current version arguments: id | CompletionKit com.completionkit |
| prompts_suggest_improvementanswers Suggest an improved version of a prompt, grounded in a run's test results and judge feedback. Analyzes the run's responses, scores, and reviews, then returns reasoning plus a rewritten template (preserving {{variables... arguments: run_id | CompletionKit com.completionkit |
| dossier_llms_txtanswers Core dossier check: Detect whether a domain publishes an llms.txt at its root — the emerging convention that gives AI agents a curated markdown index of a site's content. Use in a content-posture audit to confirm a si... arguments: domain | com.domainposture/mcp com.domainposture |
| get_prompt_templateanswers Return the rendered text of one of this server's guided prompts (mcp-demo-tour, tar-matter-kickoff, weekly-digest). Use when the client can call tools but cannot open MCP prompts directly, or when you want to inspect ... arguments: prompt_name, audience, matter_description, week_start | eDiscovery Decoder News/Calc com.ediscoverydecoder |
| calculate_installmentanswers Calcula parcelas de financiamento/empréstimo pelo sistema Price (parcelas fixas). Parâmetros obrigatórios: total, installments, monthly_rate. Use exatamente estes nomes, em inglês. arguments: total, installments, monthly_rate | FalaZuki Finance BR com.falazuki |
| get_llms_fullanswers Return a link to the complete, source-linked agent reference (llms-full.txt) for a product — its entire prose, API surface, and examples in one document. This file is large, so it is returned as a resource link and ca... arguments: product | com.freebatteryfactory/docs com.freebatteryfactory |
| get_llms_txtanswers Get the auto-generated llms.txt for a cataloged company: a curated, AI-readable guide to the business. arguments: domain | com.groundedaeo/grounded-aeo com.groundedaeo |
| send_quote_completion_linkanswers Sends a checkout/completion link to the customer for a specific quote (by quote key) via SMS and/or Email. THIS MESSAGES THE CUSTOMER. arguments: quoteKey, body | HireAHelper Moving Services com.hireahelper |
| get_quote_completion_urlanswers Gets the completion (checkout) URL for a specific quote by quote key, WITHOUT sending an email or SMS to the customer. arguments: quoteKey | HireAHelper Moving Services com.hireahelper |
| get_llms_txtanswers Fetch /llms.txt (full mirror; optional offset/limit lines) arguments: offset, limit | ikeytz – Schlüsseldienst Ludwigsburg com.ikeytz |
| get_llms_mcp_serveranswers Fetch /llms-mcp-server.txt — public Server-MCP tool catalog (all tools, how to call, no login). arguments: offset, limit | ikeytz – Schlüsseldienst Ludwigsburg com.ikeytz |
| get_llms_mcp_webanswers Fetch /llms-mcp-web.txt — public WebMCP (browser) tool catalog (bootstrap + full list, no login). arguments: offset, limit | ikeytz – Schlüsseldienst Ludwigsburg com.ikeytz |
| get_llms_txtanswers AI-readable overview of the site (llms.txt). Returns: ok. | Invokera Status com.invokera |
| post_chat_completionsanswers Creates a model response for the given chat conversation Billing per call: Credits: metered (~0 avg). arguments: body | com.jojapi/gpt-5 com.jojapi |
| post_embeddingsanswers Creates an embedding vector representing the input text. Group: Embeddings. Billing per call: Credits: metered. arguments: body | com.jojapi/swift-ai com.jojapi |
| generate_elevenlabs_agent_promptanswers Generates a production-grade system prompt for an ElevenLabs conversational agent acting as a business phone receptionist: identity, job, voice style, booking flow, guardrails, and escalation rules. arguments: biz, agentName, industry, tasks, hours, spanish | com.lobbyvoices/receptionist-toolkit com.lobbyvoices |
| parent_list_enrollmentsanswers Returns the parent's enrollments (programs their kid is signed up for). | Lodi Kids Activities com.lodikidsactivities |
| parent_report_enrollmentanswers Self-report that the parent enrolled their kid in a program outside the LKA RegFlow ('I'm In'). Accepts program UUID or slug. arguments: program_id, kid_id | Lodi Kids Activities com.lodikidsactivities |
| installment_plananswers Split a total into an installment schedule with optional deposit. PREMIUM (license).
Rounding remainders land on the final payment so the schedule always
sums exactly. Typical input {"total": 1000, "installments": 3,... arguments: total, installments, deposit_pct | Moltline Merchant Maths com.moltlinestudio |
| send_quote_completion_linkanswers Sends a checkout/completion link to the customer for a specific quote (by quote key) via SMS and/or Email. THIS MESSAGES THE CUSTOMER. arguments: quoteKey, body | MovingPlace Moving Services com.movingplace |
| get_quote_completion_urlanswers Gets the completion (checkout) URL for a specific quote by quote key, WITHOUT sending an email or SMS to the customer. arguments: quoteKey | MovingPlace Moving Services com.movingplace |
| neblla_get_llmsanswers Fetch the Neblla developer guide (llms.txt), split by topic so you read only what the app uses. Call this BEFORE designing or writing any code for a Neblla app — at minimum the `core` section (the default): the canoni... arguments: section, offset, maxLength | Neblla com.neblla |
https://neuronto.com/tools?q=..., or connect an agent to
https://neuronto.com/mcp and call find_tool. No key, no signup.