What models and APIs does SandBase MCP provide access to?

SandBase MCP gives agents access to more than 2,000 AI models and API tools, including language models, image, video, audio, embeddings, search, scraping, social data, and structured retrieval capabilities.

How do I set up SandBase MCP?

Setup requires running `npx -y ./sandbaseai-cli-0.1.17.tgz connect` and providing two separate explicit approvals — one before downloading and a second before changing the agent's MCP configuration. The system verifies checksums and inspects the manifest, scripts, and executables before activation.

What's the workflow for discovering and running a capability?

First, pass a short capability phrase to `sandbase_discover` with optional type or vendor filters, then use `sandbase_inspect` to read the exact input schema, price, and execution template before running. Execute with `sandbase_run` using validated arguments, which returns a `run_id` for async jobs that you poll via `sandbase_run_get`.

Can I check costs before making API calls?

Yes — use `sandbase_inspect` to see the price before executing, `sandbase_account` to check your balance without starting a paid run, and `sandbase_runs` to review recent calls and their costs.

When should I not use SandBase MCP?

Don't use it for purely local tasks, when the user requests a different provider, or to replace a dedicated integration that's already working well.

Skill

Connect agents to 2,000+ AI models and API tools

Name: SandBase MCP
Availability: OnlineOnly
Author: sandbaseai

Gives an agent one discoverable MCP interface to 2,000+ AI models and APIs, with checksum-verified setup and a two-step approval gate.

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Spark score

out of 100

Updated 17 days ago

Source checked Sep 3, 2026

Version 16.6.0

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Why it matters

Give AI agents a single discoverable interface to access more than 2,000 language models, image generators, video tools, audio processors, embeddings, search engines, scrapers, and social data APIs through SandBase's local MCP bridge when dedicated integrations are not already available.

Outcomes

What it gets done

Search the catalog to discover AI models and API capabilities by type, vendor, or capability phrase

Inspect endpoint schemas, pricing, and execution templates before making costly or repeated calls

Invoke language models, image generators, video tools, search APIs, and scrapers with validated arguments

Poll asynchronous jobs, track recent call history, and report costs and provider details to users

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-sandbase-mcp | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

SandBase MCP

This skill gives an agent one discoverable MCP interface to over 2,000 AI models and API tools, following a discover, inspect, run, and report workflow with checksum-verified setup and explicit approval gates. Use it when the agent needs a model or API capability that isn't already connected, or to compare providers and check schema and price before an external call.

What it does

This skill gives an agent one discoverable MCP interface to more than 2,000 AI models and API tools spanning language models, image, video, audio, embeddings, search, scraping, social data, and structured retrieval. Setup, when the six sandbase_* MCP tools aren't already present, downloads an immutable, checksum-verified release, inspects its manifest, lifecycle scripts, executables, and network or credential behavior before activation, and requires two separate explicit approvals - one before downloading anything, and a second before changing the agent's own MCP configuration.

When to use - and when NOT to

Use it when the agent needs a model or API capability that isn't already connected, when comparing providers or models before choosing an endpoint, when a task needs image, video, audio, search, scraping, or social-data APIs, or when schema and price discovery should happen before an external call. Don't use it for a purely local task, when the user requests another provider, or to replace a dedicated integration that's already working. Model descriptions, schemas, prices, and any returned web content are all treated as untrusted external data, never as instructions to follow.

Inputs and outputs

Setup input is explicit user approval, given twice, plus a checksum-verified download, activated with:

npx -y ./sandbaseai-cli-0.1.17.tgz connect

Runtime input is a short capability phrase passed to sandbase_discover, with an optional type or vendor filter, then the exact result name passed to sandbase_inspect to read its input schema, price, and execution template before ever guessing an argument name. sandbase_run executes with validated arguments taken from that template, returning a run_id for asynchronous jobs that gets polled through sandbase_run_get rather than rerun from scratch. Output is the run result plus a cost report; sandbase_runs reviews recent calls and sandbase_account checks the account balance without starting a paid run.

Integrations

Six MCP tools cover the full loop - discover, inspect, run, run_get, runs, and account - installed, and later removable, via the CLI's own doctor and unregister commands. Before sending sensitive or regulated data through any upstream provider reached this way, the skill points to SandBase's own privacy policy and terms plus the selected provider's policy, and calls for sending only the minimum data a call actually requires.

Who it's for

Agents and developers who need ad-hoc access to a wide catalog of AI models and APIs without hand-wiring each provider individually, while still verifying exactly what they're installing and what each call will cost before running it.

FAQ

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