--- title: "Developer API, MCP & SDK Documentation | Aguasto" description: "Integrate Aguasto's public research search and educational calculators through HTTP, MCP, browser tools and downloadable agent skills. No GPU service credentials are issued." canonical: "https://www.aguasto.com/developers" last-updated: "2026-10-27" --- # Developer API, MCP & SDK Documentation | Aguasto Canonical HTML: [Developers](https://www.aguasto.com/developers) > Integrate Aguasto's public research search and educational calculators through HTTP, MCP, browser tools and downloadable agent skills. No GPU service credentials are issued. Developers / Public research ## Aguasto research. APIs and tools for agents. Use Aguasto’s published research and educational calculations from your application or AI assistant. Public access requires no API key. ### Available today Search descriptions of our public GPU, model and infrastructure references, follow their original sources, and run the same educational formulas as our [engineering tools](https://www.aguasto.com/tools) . The platform is in development: GPU capacity is not bookable, and inference, reservations, service accounts, API keys and token pricing are unavailable. ### HTTP quickstart Send JSON requests over HTTPS. The [OpenAPI description](https://www.aguasto.com/openapi.json) defines request fields, responses and errors. [The public page catalog](https://www.aguasto.com/api/v1/catalog) includes canonical references and modification dates. curl https://www.aguasto.com/ask \\ -H 'Content-Type: application/json' \\ -d '{"query":{"text":"GPU rack cooling"},"prefer":{"mode":"list"}}' curl https://www.aguasto.com/api/v1/calculate \\ -H 'Content-Type: application/json' \\ -d '{"calculation":"weight\_memory","parameters":{"parameters\_billions":70,"bits":4}}' Search uses deterministic matching of published titles and descriptions. It returns references, rather than generated answers. Follow each canonical page for full evidence. Request prefer.streaming: true for NLWeb 0.55 events: start, result or error, and complete. For smaller catalog responses, request /api/v1/catalog?limit=20&offset=0. Continue with pagination.next\_offset until it is null. Omitting pagination parameters returns the complete current inventory. #### Read several page records in one request Send up to 20 unique published paths to /api/v1/pages/batch. Results preserve your order and include metadata and canonical links. Unknown paths receive a per-item 404 while valid records still return. External URLs, query strings and duplicate paths are rejected. This reads public metadata without fetching arbitrary URLs or storing inputs. curl https://www.aguasto.com/api/v1/pages/batch \\ -H 'Content-Type: application/json' \\ -d '{"paths":\["/gpus","/models"\]}' ### Try a public request This playground searches public descriptions or calculates an illustrative estimate. It has no customer data, account or provisioning actions. Don’t enter confidential information. The application does not store these inputs; hosting providers may process technical request metadata. Search published researchSearch research Try 70B / 4-bit estimate Choose a request to see its JSON response. ### Connect with MCP Add a Streamable HTTP server in your MCP client using one of these URLs. Both are anonymous and read-only. Use the tools advertised by the live tools/list response. - https://www.aguasto.com/mcp/docs — search published page descriptions and retrieve canonical references. - https://www.aguasto.com/mcp/tools — estimate weight memory, facility energy or completed-token cost. {"mcpServers":{"aguasto-research":{"type":"http","url":"https://www.aguasto.com/mcp/docs"},"aguasto-calculators":{"type":"http","url":"https://www.aguasto.com/mcp/tools"}}} Client configuration formats vary. MCP requests use Accept: application/json, text/event-stream. Responses use stateless JSON; server-initiated SSE and sessions are not offered. See [research server card](https://www.aguasto.com/mcp/docs/server-card) and [calculator server card](https://www.aguasto.com/mcp/tools/server-card) . Verified MCP Registry records: [Aguasto Research](https://registry.modelcontextprotocol.io/v0.1/servers/com.aguasto.www%2Fdocs/versions/1.0.0) and [Aguasto Engineering Calculators](https://registry.modelcontextprotocol.io/v0.1/servers/com.aguasto.www%2Ftools/versions/1.0.0) . The registry identifiers use the verified www.aguasto.com hostname. Client connections continue to use the HTTPS endpoints above. You can also find the public research server in the [Aguasto Research listing on MCP.so](https://mcp.so/servers/aguasto-research) , with the direct endpoint, authentication setting and client setup instructions. ### Agent skills and browser tools Read the [research skill](https://www.aguasto.com/agent-plugin/skills/aguasto-research/SKILL.md) or [calculation skill](https://www.aguasto.com/agent-plugin/skills/aguasto-calculations/SKILL.md) . The [plugin manifest](https://www.aguasto.com/agent-plugin/plugin.json) and [MCP configuration](https://www.aguasto.com/agent-plugin/mcp.json) describe the package. The [official public integration repository](https://github.com/Altssdata/aguasto-agents) contains standalone clients, skills, plugin configuration and usage rules. It excludes the private corporate website source and retains the existing rights-reserved terms. Install the verified public skills with npx skills add Altssdata/aguasto-agents --skill aguasto-research aguasto-calculations. Directory pages: [research](https://skills.sh/altssdata/aguasto-agents/aguasto-research) and [calculations](https://skills.sh/altssdata/aguasto-agents/aguasto-calculations) . In browsers that implement WebMCP, the site registers public research search and weight-memory calculation tools through document.modelContext, with a navigator.modelContext compatibility fallback. Ordinary navigation and calculators work when this experimental API is absent. ### Install the Go client or Homebrew CLI The [official Go SDK](https://github.com/Altssdata/aguasto-agents/tree/main/go) uses the standard library and supports public catalog, search and calculation requests. The Homebrew formula installs the SHA-256-pinned JavaScript release with Node.js. Both expose the same public research interfaces. go install github.com/Altssdata/aguasto-agents/go/cmd/aguasto-research@v1.0.0 brew tap altssdata/aguasto-agents https://github.com/Altssdata/aguasto-agents brew install altssdata/aguasto-agents/aguasto-research aguasto-research fixtures aguasto-research search "GPU rack cooling" ### JavaScript SDK and CLI on npm Install the [official aguasto-research npm package](https://www.npmjs.com/package/aguasto-research) with Node.js 22 or newer. The package and CLI support public search, page references and educational calculations. npm install aguasto-research@1.0.0 npx aguasto-research fixtures npx aguasto-research search "GPU rack cooling" #### Source archives and Python client The [JavaScript SDK and CLI](https://www.aguasto.com/developers/downloads/aguasto-research-js.tgz) and [Python SDK and CLI](https://www.aguasto.com/developers/downloads/aguasto-research-python.tar.gz) are versioned source packages for these public interfaces. The JavaScript package is also published on npm; the Python client is available as a source download and is not published on PyPI. You can install either downloaded archive locally. See the included README and use the CLI’s --help command. Never install a similarly named registry package on the assumption it is official. To use the Aguasto command-line interface with Node.js 22 or newer, download the JavaScript archive above into your project, then run: The same standalone SDK/CLI source archives are available in the [verified v1.0.0 public integration release](https://github.com/Altssdata/aguasto-agents/releases/tag/v1.0.0) . npm install ./aguasto-research-js.tgz npx --no-install aguasto-research --help npx --no-install aguasto-research fixtures npx --no-install aguasto-research search "GPU rack cooling" ### Access, errors and safe testing The [integration testing skill](https://www.aguasto.com/agent-plugin/skills/aguasto-integration-testing/SKILL.md) walks through synthetic search, empty matches, calculation results and invalid-input errors. Install it with npx skills add Altssdata/aguasto-agents --skill aguasto-integration-testing, or view its [skills.sh directory page](https://skills.sh/altssdata/aguasto-agents/aguasto-integration-testing) . It distinguishes offline fixture checks from live API verification and ChatGPT-host testing. Use the [Aguasto integration sandbox](https://www.aguasto.com/sandbox) to test fixed synthetic search records, empty results, calculator inputs and HTTP errors at /api/v1/sandbox. It requires no account, reads no production research records and stores no inputs. See [public access instructions](https://www.aguasto.com/auth.md) . No login or token exchange is required. JSON bodies are limited to 16 KiB; queries to 2,000 characters and 20 results. Malformed inputs fail with structured errors. Cross-origin browser access is restricted to the configured site origin. No credential or paid compute flow is exposed. The playground and calculators do not change production data. For offline integration tests, the downloadable packages include fixtures; they are not a customer GPU sandbox. Search and calculation requests never call a model provider. ### API versioning and deprecation policy The [public API versioning and deprecation policy](https://www.aguasto.com/developers/versioning) is available as an indexed HTML page with its own Markdown alternate. The [standalone versioning and deprecation policy](https://www.aguasto.com/versioning.md) records the current contract, compatibility rules and retirement notices. Catalog, batch and calculator responses link it with Link: rel="deprecation"; that relation makes the policy discoverable without declaring an active operation deprecated. REST operations use the /api/v1 major version. New optional fields may be added within v1; clients should ignore unknown response fields. Breaking request or response changes use a new major URL version. Before removing a deprecated operation, we will document its replacement and planned removal date here at least 90 days ahead and advertise that date with Deprecation and Sunset response headers. No v1 operation is currently deprecated. REST errors use a stable Error schema with error.code and error.message. Invalid inputs return 400; unknown v1 endpoints return JSON with HTTP 404. NLWeb retains its own documented failure envelopes, and MCP retains JSON-RPC errors. #### Retrying calculations with a content-bound key Calculator POST requests evaluate pure v1 functions and do not change server data. Retrying the same inputs produces the same result across instances. For an explicit input-consistency check, send an optional Idempotency-Key structured string containing "sha256:\". The digest is SHA-256 of the compact UTF-8 JSON envelope, with root keys calculation then parameters and parameter keys in lexicographic order, using JavaScript JSON number/string encoding. For example: import { createHash } from "node:crypto"; const payload = JSON.stringify({calculation:"weight\_memory",parameters:{bits:4,parameters\_billions:70}}); const key = '"sha256:' + createHash("sha256").update(payload).digest("hex") + '"'; const response = await fetch("https://www.aguasto.com/api/v1/calculate", { method:"POST", headers:{"Content-Type":"application/json","Idempotency-Key":key}, body:payload }); The server verifies the content binding and echoes a valid key. Malformed keys return 400; a key paired with different inputs returns 422. Keys have no time-based expiry within the v1 formula contract. The server recomputes these deterministic estimates; it stores neither request keys nor inputs and offers no stored-job or transaction replay service. Requests without this optional header continue to work. REST responses expose RateLimit-Limit, RateLimit-Remaining and RateLimit-Reset (seconds until reset), plus compatibility X-RateLimit-\* fields whose reset value uses Unix seconds. Within each warm server instance, catalog, index and batch page reads share 120 requests per minute and calculations allow 30, keyed by a temporary hash of the provider-supplied client address. Exceeding a window returns 429 and Retry-After. These limits reset on cold starts and apply independently across instances; they are best-effort admission controls, not global quotas. The hosting platform may impose additional limits. ### Discovery and reference data Catalog provenance links pin the public skill sources and MCP client configuration to a reviewed GitHub revision with SHA-256 digests. This publisher metadata is unsigned advisory information; it does not assert compliance certification or cryptographically verified publisher identity. - [Agentic Resource Discovery catalog](https://www.aguasto.com/.well-known/ard.json) - [API catalog](https://www.aguasto.com/.well-known/api-catalog) and [skill index](https://www.aguasto.com/.well-known/agent-skills/index.json) - [Website index](https://www.aguasto.com/llms.txt) and [developer index](https://www.aguasto.com/developers/llms.txt) - [Schema.org feed](https://www.aguasto.com/schema-feed.jsonl) and [Schema Map](https://www.aguasto.com/schemamap.xml) - [Structured agent view](https://www.aguasto.com/?mode=agent) For future enterprise compute requirements, use our [contact instructions](https://www.aguasto.com/contact) . Online delivery remains disabled; an inquiry draft is unsent until you send it using your own email application. ## Company navigation - [API versioning policy](https://www.aguasto.com/developers/versioning) - [Developer sandbox](https://www.aguasto.com/sandbox) - [Home](https://www.aguasto.com/) - [Infrastructure](https://www.aguasto.com/infrastructure) - [GPU Cloud](https://www.aguasto.com/gpu-cloud) - [AI Inference](https://www.aguasto.com/ai-inference) - [Development & Investment](https://www.aguasto.com/development) - [Company](https://www.aguasto.com/company) - [Contact](https://www.aguasto.com/contact) - [Privacy](https://www.aguasto.com/privacy) - [Research topics](https://www.aguasto.com/topics) - [GPU systems & local clusters](https://www.aguasto.com/topics/gpu-systems) - [Model serving & inference](https://www.aguasto.com/topics/model-serving) - [Modular infrastructure & resources](https://www.aguasto.com/topics/modular-infrastructure) - [Compute economics & evidence](https://www.aguasto.com/topics/compute-economics) - [Glossary](https://www.aguasto.com/glossary) - [Methodologies](https://www.aguasto.com/methodologies) - [Developers](https://www.aguasto.com/developers) - [Resources](https://www.aguasto.com/resources) - [GPU Library](https://www.aguasto.com/gpus) - [Model Index](https://www.aguasto.com/models) - [Inference Stack](https://www.aguasto.com/inference-stack) - [Learn](https://www.aguasto.com/learn) - [Insights](https://www.aguasto.com/insights) - [Research](https://www.aguasto.com/research) - [Industry cases](https://www.aguasto.com/research/cases) - [Why Modular](https://www.aguasto.com/infrastructure/why-modular) - [Cooling & Resource Efficiency](https://www.aguasto.com/infrastructure/cooling-resource-efficiency) - [Partners](https://www.aguasto.com/partners) - [GPU comparison](https://www.aguasto.com/gpus/compare) - [Model comparison](https://www.aguasto.com/models/compare) - [Engineering tools](https://www.aguasto.com/tools) - [Development friction](https://www.aguasto.com/research/hyperscale-development-friction) - [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100) - [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200) - [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200) - [NVIDIA B300](https://www.aguasto.com/gpus/b300) - [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72) - [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72) - [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72) - [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x) - [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x) - [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x) - [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x) - [AMD Helios](https://www.aguasto.com/gpus/helios) - [GLM-4.5](https://www.aguasto.com/models/glm-4-5) - [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b) - [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1) - [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct) - [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it) - [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b) - [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) - [GLM-5.3](https://www.aguasto.com/models/glm-5-3) - [Qwen3.8-27B](https://www.aguasto.com/models/qwen3-8-27b) - [DeepSeek-V4.1-Flash](https://www.aguasto.com/models/deepseek-v4-1-flash) - [Llama 4 Scout 17B-16E Instruct](https://www.aguasto.com/models/llama-4-scout-17b-16e-instruct) - [Gemma 4 31B IT](https://www.aguasto.com/models/gemma-4-31b-it) - [Kimi K3](https://www.aguasto.com/models/kimi-k3) - [vLLM](https://www.aguasto.com/inference-stack/vllm) - [SGLang](https://www.aguasto.com/inference-stack/sglang) - [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) - [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) - [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton) - [AMD ROCm](https://www.aguasto.com/inference-stack/rocm) - [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve) - [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) - [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory) - [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology) - [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) - [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) - [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) - [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics) - [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability) - [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures) - [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) - [What Is a Modular AI Data Center?](https://www.aguasto.com/learn/what-is-a-modular-ai-data-center) - [How Modular AI Data Centers Are Built and Commissioned](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning) - [Liquid Cooling for High-Density GPU Systems: Follow the Interfaces](https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces) - [Why Electricity Availability Shapes AI Infrastructure Deployment](https://www.aguasto.com/research/electricity-availability-and-time-to-power) - [What Is KV Cache? Capacity, Reuse and Isolation](https://www.aguasto.com/learn/kv-cache-capacity-and-policy) - [Continuous Batching: Throughput Without Hiding Latency](https://www.aguasto.com/learn/continuous-batching-and-latency) - [Applied Digital: Reading the Polaris Forge 1 Delivery Milestones](https://www.aguasto.com/research/cases/applied-digital-polaris-forge-capacity-milestones) - [Crusoe Abilene: Separate Delivered Infrastructure from Expansion Plans](https://www.aguasto.com/research/cases/crusoe-abilene-delivery-and-expansion) - [Data Center Cooling: Choosing Air, Liquid, Dry and Hybrid Architectures](https://www.aguasto.com/learn/data-center-cooling-air-liquid-dry-hybrid) - [Can Data Centers Reduce Water Consumption? Define the Water Boundary First](https://www.aguasto.com/research/data-center-water-consumption-and-withdrawal) - [Measuring PUE, WUE and AI Compute Efficiency at the Right Boundary](https://www.aguasto.com/learn/measuring-pue-wue-and-ai-compute-efficiency) - [The Environmental Trade-offs of High-Density AI Infrastructure](https://www.aguasto.com/research/environmental-tradeoffs-and-infrastructure-reuse) - [Selecting GPU Systems for Enterprise Inference](https://www.aguasto.com/learn/selecting-enterprise-inference-gpu-systems) - [Networking Rack-Scale AI Systems: Define the Communication Boundary](https://www.aguasto.com/learn/rack-scale-ai-networking-boundaries) - [Official LinkedIn](https://www.linkedin.com/company/aguasto) This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.