Provides tools for discovering Zapier automations, searching the app directory, and finding popular Zap templates with one-click create links.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@zapier-discovery-mcp What do people automate between Slack and Notion?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server for automation discovery. Zapier MCP executes automations — this server answers the question that comes first: what could I automate?
"What do people automate between Slack and Notion?" — real, popular Zap templates, each with a one-click create link
"Is there a Zapier integration for this tool?" — search the 9,000+ app directory
"Give me automation ideas for my CRM" — templates ranked by actual usage, not hallucinated
Sibling project: zapier-dev-mcp for building Zapier integrations.
You need a Zapier Partner API client_id — free with any Zapier integration, and not a secret (it ships in client-side embed code). See the Partner API docs for where to find yours.
Claude Code
claude mcp add zapier-discovery --env ZAPIER_CLIENT_ID=your_client_id -- npx -y zapier-discovery-mcpClaude Desktop — add to claude_desktop_config.json:
{
"mcpServers": {
"zapier-discovery": {
"command": "npx",
"args": ["-y", "zapier-discovery-mcp"],
"env": { "ZAPIER_CLIENT_ID": "your_client_id" }
}
}
}Optional: ZAPIER_API_BASE overrides the API host (testing, proxies).
Related MCP server: mcp-server-mcpindex
Tool | What it does |
| Search the app directory by name; popularity-ordered without a query. Returns slugs for the next step. |
| Popular ready-made Zap templates for one app or a pair — title, step chain ( |
Defensive parsing. Partner API responses are only partially documented; the client whitelists fields and handles both bare-array and enveloped responses rather than assuming shapes.
Testable without credentials. The smoke test runs a local HTTP mock of the Partner API and points the server at it via ZAPIER_API_BASE — full end-to-end over stdio with zero secrets. Missing ZAPIER_CLIENT_ID produces setup instructions as a tool error, not a crash.
HTML in titles/descriptions is stripped and truncated before it reaches the model.
Not affiliated with or endorsed by Zapier.
npm install
npm test # offline unit tests (mocked fetch)
npm run build # tsc → dist/
node scripts/smoke.mjs # end-to-end against a local Partner API mockMIT
What do people actually automate with these apps? Returns popular, ready-made Zap templates (by app slug from search_apps — one app or a pair like ['slack','notion']), each with a step chain and a one-click create link.
| Name | Required | Description | Default |
|---|---|---|---|
| apps | Yes | App slugs, e.g. ['slack', 'notion'] | |
| limit | No | Max templates |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the tool returns templates with step chains and one-click create links, and that input comes from app slugs. It does not mention sorting, pagination, or failure modes, but the read-style behavior is reasonably clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single well-structured sentence front-loads the purpose, adds a motivating question, and packs in the key details about input format and output contents without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter lookup with full schema coverage, the description provides enough context about what is returned. It lacks explicit notes on default limit behavior or result ordering, but these are minor for this tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents apps and limit with defaults and ranges. The description adds a cross-reference to search_apps and example app pairs, but does not meaningfully expand beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action — returning popular, ready-made Zap templates — and identifies the input as app slugs from search_apps. It clearly distinguishes this from searching apps by focusing on templates with step chains and one-click create links.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the workflow: first use search_apps to get app slugs, then use this tool to find templates for those apps. It does not explicitly say when not to use it, but the single sibling's purpose is clear enough to avoid confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Zapier's directory of 9,000+ integrated apps by name. Without a query, returns the most popular apps. Use the returned slugs with find_zap_templates.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results | |
| query | No | App name to search for, e.g. 'notion' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosure. It reveals an important behavioral nuance: 'Without a query, returns the most popular apps.' It also discloses that results contain slugs usable elsewhere. It does not state return structure, auth needs, or rate limits, but for a search tool the core behavior is covered. This is above the minimum viable level.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero filler. The main purpose is front-loaded, the default behavior is stated succinctly, and the integration with find_zap_templates is included in a single clause. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only search tool with only two optional parameters and no output schema, the description is nearly complete. It covers the purpose, default behavior, and output usage. It doesn't specify the exact response shape, but 'returned slugs' implies the necessary output field. A fully self-contained description might mention the response format, but this is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes both parameters thoroughly (limit: max results with min/max/default; query: app name to search for). Schema description coverage is 100%, so the description doesn't need to compensate. The phrase 'returned slugs' hints at the query parameter's purpose but adds little beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Search'), a precise resource ('Zapier's directory of 9,000+ integrated apps'), and the search method ('by name'). It also hints at the tool's role in a pipeline by mentioning 'returned slugs' with find_zap_templates, which distinguishes it from that sibling without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: it is the tool to use for discovering apps and obtaining slugs, with an explicit pointer to the sibling tool ('Use the returned slugs with find_zap_templates'). It doesn't explicitly state when not to use it, but the chaining instruction effectively communicates the workflow. No exclusions are mentioned, so it's not a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0find_zap_templatessearch_appsThe two tools have clearly distinct purposes: one searches/returns apps, the other retrieves Zap templates for a given app or app pair. There is no overlap or ambiguity in what each tool does.
Both tool names follow a consistent verb_noun pattern: search_apps and find_zap_templates. The naming is predictable and aligned with their respective actions.
With only two tools, the server feels minimal but arguably well-scoped for its stated discovery purpose. It is exactly at the borderline between 'too few' and 'appropriately lean' for the domain.
The core discovery workflow (search apps, then find templates for those apps) is covered, but there are noticeable gaps such as searching templates by keyword/use case or getting detailed app information. Agents can complete the basic flow but may hit dead ends for broader discovery scenarios.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Hosted MCP server connecting AI assistants to 9,000+ apps and 40,000+ actions via Zapier.
Connect to 1,400+ apps and 15,000+ actions through one OAuth-protected MCP server.
Automate 1,000+ services from any MCP-compatible AI agent: build Applets, run actions and queries.
Zapier MCP connects AI tools like Claude, ChatGPT, and Cursor to over 8,000 apps and 30,000+ actions, enabling AI to perform real-world tasks such as sending messages, searching data, scheduling events, and updating records. It acts as a translator between AI tools and apps, handling authentication, rate limits, and retries automatically, transforming AI from a conversational tool into a functional extension of your business stack.
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