Allows sending and reading emails, and retrieving recent messages via Gmail.
Allows managing Linear users, projects, and issues.
Allows fetching and displaying QuickBooks invoices and financial data.
Allows retrieving products and managing Shopify store data.
Allows sending messages to Slack channels and integrating with Slack workspaces.
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., "@Pica MCP Server get my last 5 emails from Gmail"
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.
A Model Context Protocol (MCP) server that integrates with Pica, enabling seamless interaction with various third-party services through a standardized interface. This server provides direct access to platform integrations, actions, execution capabilities, and robust code generation capabilities.
list_pica_integrations - List all available platforms and your active connections
get_pica_platform_actions - Get available actions for a specific platform
get_pica_action_knowledge - Get detailed documentation for a specific action including parameters and usage
execute_pica_action - Execute API actions with full parameter support
Related MCP server: anyapi-mcp-server
Connect to 100+ platforms through Pica
Manage multiple connections per platform
Access real-time connection status
Execute actions immediately (e.g. "read my last gmail email", "send a message to the slack channel #general")
Generate integration code (e.g. "build a form to send emails using gmail", "create a UI for messaging")
Intelligent context handling
Never exposes secrets in generated code
Uses environment variables: PICA_SECRET, PICA_[PLATFORM]_CONNECTION_KEY
Sanitized request configurations for production use
Execute actions directly through the MCP interface
Support for all HTTP methods (GET, POST, PUT, DELETE, etc.)
Handle form data, URL encoding, and JSON payloads
Pass path variables, query parameters, and custom headers
All requests authenticated through Pica's secure proxy
No need to manage individual platform API keys
Environment variable configuration for security
npm install @picahq/mcpPICA_SECRET=your-pica-secret-keyGet your Pica secret key from the Pica dashboard.
npx @picahq/mcpTo use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"pica": {
"command": "npx",
"args": ["@picahq/mcp"],
"env": {
"PICA_SECRET": "your-pica-secret-key"
}
}
}
}In the Cursor menu, select "MCP Settings" and update the MCP JSON file to include the following:
{
"mcpServers": {
"pica": {
"command": "npx",
"args": ["@picahq/mcp"],
"env": {
"PICA_SECRET": "your-pica-secret-key"
}
}
}
}Build the Docker Image:
docker build -t pica-mcp-server .Run the Docker Container:
docker run -e PICA_SECRET=your_pica_secret_key pica-mcp-serverTo install pica for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @picahq/mcp --client claudeYou can deploy this MCP server to Vercel for remote access:
Install dependencies including Vercel adapter:
npm install @vercel/mcp-adapter zodDeploy to Vercel:
vercelConfigure your MCP client to use the remote server:
For Cursor: https://your-project.vercel.app/api/mcp
For Claude/Cline: Use npx mcp-remote https://your-project.vercel.app/api/mcp
See DEPLOYMENT.md for detailed Vercel deployment instructions.
Build Email Form:
"Create me a React form component that can send emails using Gmail using Pica"
Linear Dashboard:
"Create a dashboard that displays Linear users and their assigned projects with filtering options using Pica"
QuickBooks Table:
"Build a paginatable table component that fetches and displays QuickBooks invoices with search and sort using Pica"
Slack Integration:
"Create a page with a form that can post messages to multiple Slack channels with message scheduling using Pica"
Gmail Example:
"Get my last 5 emails from Gmail using Pica"
Slack Example:
"Send a slack message to #general channel: 'Meeting in 10 minutes' using Pica"
Shopify Example:
"Get all products from my Shopify store using Pica"
list_pica_integrationsList all available Pica integrations and platforms. Always call this tool first to discover available platforms and connections.
Parameters: None
Returns:
Connected integrations grouped by platform
Available platforms with descriptions
Summary statistics
Management links
get_pica_platform_actionsGet all available actions for a specific platform.
Parameters:
platform (string, required): Platform name in kebab-case format (e.g., 'ship-station', 'shopify')
Returns:
List of available actions with IDs and titles
Platform-specific action count
Next steps guidance
get_pica_action_knowledgeGet comprehensive documentation for a specific action. Must be called before execute_pica_action to understand requirements.
Parameters:
action_id (string, required): Action ID from get_pica_platform_actions
platform (string, required): Platform name in kebab-case format
Returns:
Detailed action documentation
Parameter requirements and structure
API-specific guidance and caveats
Usage examples and implementation notes
execute_pica_actionExecute a Pica action to perform operations on third-party platforms. Critical: Only call this when the user wants to execute an action, not when building applications.
Parameters:
platform (string, required): Platform name
action (object, required): Action object with _id, path, and method
connectionKey (string, required): Connection key for the platform
data (object, optional): Request body data
pathVariables (object, optional): Variables to replace in the path
queryParams (object, optional): Query parameters
headers (object, optional): Additional headers
isFormData (boolean, optional): Send as multipart/form-data
isFormUrlEncoded (boolean, optional): Send as URL-encoded form data
Returns:
requestConfig: Sanitized request configuration
responseData: Actual API response from the platform
The server implements comprehensive error handling:
✅ Parameter validation for all tools
✅ Connection verification before execution
✅ Path variable validation and substitution
✅ Graceful handling of API failures
✅ Detailed error messages for debugging
✅ MCP-compliant error responses
🔐 Single environment variable required: PICA_SECRET
🛡️ All requests authenticated through Pica's secure proxy
🔒 No direct platform API key management needed
🚫 Secrets never exposed in responses
✅ Request configurations sanitized
🔍 Sensitive data filtered from logs
🛡️ Input validation and sanitization
GPL-3.0
For support, please contact support@picaos.com or visit https://picaos.com
Execute a Pica action to perform actual operations on third-party platforms. CRITICAL: Only call this when the user's intent is to EXECUTE an action (e.g., 'read my last Gmail email', 'fetch 5 contacts from HubSpot', 'create a task in Asana'). DO NOT call this when the user wants to BUILD or CREATE code/forms/applications - in those cases, stop after get_pica_action_knowledge and provide implementation guidance instead. REQUIRED WORKFLOW: Must call get_pica_action_knowledge first. If uncertain about execution intent or parameters, ask for confirmation before proceeding.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | Platform name | |
| action | Yes | Action object with ID, path, and method | |
| connectionKey | Yes | Key of the connection to use | |
| data | No | Request data (for POST, PUT, etc.) | |
| pathVariables | No | Variables to replace in the path | |
| queryParams | No | Query parameters | |
| headers | No | Additional headers | |
| isFormData | No | Whether to send data as multipart/form-data | |
| isFormUrlEncoded | No | Whether to send data as application/x-www-form-urlencoded |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It discloses critical behavioral constraints: only execute when user intends to execute, not for building. It mentions workflow and caution. However, it lacks details on success/error responses, rate limits, or side effects.
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?
The description is concise (3 sentences) and front-loaded with purpose, followed by critical guidance and workflow. Every sentence adds value.
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?
Given the tool's complexity (9 parameters, nested objects), no output schema, and no annotations, the description covers when to use but lacks detail on parameter usage, expected output format, and error handling, leaving some gaps.
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 has 100% description coverage, so baseline is 3. The description does not add significant parameter-specific details beyond the schema; it focuses on tool usage rather than parameter meanings.
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 clearly states the tool's purpose: 'Execute a Pica action to perform actual operations on third-party platforms.' It further distinguishes from sibling tools by specifying the required workflow and contrasting with build/create scenarios.
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?
Explicit guidance on when to call (user intent to execute) and when not to call (user wants to build/create). Includes required workflow: must call get_pica_action_knowledge first, and ask for confirmation if uncertain.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive documentation for a specific action including parameters, requirements, and usage examples. MANDATORY: You MUST call this tool before execute_pica_action to understand the action's requirements, parameter structure, caveats, and proper usage. This loads the action documentation into context and is required for successful execution.
| Name | Required | Description | Default |
|---|---|---|---|
| action_id | Yes | The action ID to get knowledge for (from the actions list returned by get_pica_platform_actions). REQUIRED: This tool must be called before create_pica_request to load the action's documentation into context. | |
| platform | Yes | The platform name to get knowledge for (e.g., 'ship-station', 'shopify'). This is the kebab-case version of the platform name that comes from the list_pica_integrations tool AVAILABLE PLATFORMS section. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description accurately conveys it's a read operation that loads documentation into context. It does not disclose any side effects, but given the read-only nature, this is sufficiently transparent.
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?
Description is front-loaded with purpose, followed by mandatory usage instruction. Each sentence adds value, though could be slightly more concise. No fluff.
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?
No output schema, but description states it provides 'comprehensive documentation' which sufficiently covers return value. For a 2-param tool with no nested objects, the description is complete enough.
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%. The description adds value by explaining that action_id comes from get_pica_platform_actions and platform is kebab-case from list_pica_integrations, providing integration context beyond schema descriptions.
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 clearly states 'Get comprehensive documentation for a specific action', using a specific verb and resource. It distinguishes from siblings (execute, list actions, list integrations) by focusing on documentation retrieval and mandatory pre-execution role.
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?
Explicitly instructs 'You MUST call this tool before execute_pica_action' and states it is required before create_pica_request, providing clear when-to-use guidance and rationale.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all available actions for a specific platform. Call this after list_pica_integrations to discover what actions are possible on a platform. Use the exact kebab-case platform name from the integrations list. This shows you what actions are available for that platform's API.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | The platform name to get available actions for (e.g., 'ship-station', 'shopify'). This is the kebab-case version of the platform name that comes from the list_pica_integrations tool AVAILABLE PLATFORMS section. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It implies a read-only operation (get) but does not explicitly state it's non-destructive or discuss error handling (e.g., invalid platform names). It adds useful context about input format but leaves gaps.
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?
Three concise sentences: purpose first, then usage guidance, finally input specificity. No redundant words, efficient and front-loaded.
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?
The tool has no output schema, yet the description does not explain the return format or structure. It mentions 'shows you what actions are available' but lacks detail. For a simple tool, it's adequate but could be improved.
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%, but the description adds meaning beyond the schema by specifying that the platform value should be the kebab-case version from list_pica_integrations, clarifying the source and format.
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 clearly states 'Get all available actions for a specific platform', using a specific verb and resource. It distinguishes from siblings by mentioning discovery after list_pica_integrations, which sets it apart from execution or knowledge tools.
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?
Explicitly says to call after list_pica_integrations and to use the exact kebab-case platform name. This provides clear sequencing and input guidance. It does not explicitly state when not to use or alternatives, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available Pica integrations and platforms. ALWAYS call this tool first in any workflow to discover what platforms and connections are available. This returns the connections that the user has and all available Pica platforms in kebab-case format (e.g., 'ship-station', 'shopify') which you'll need for subsequent tool calls.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses return format (kebab-case platform names) and mentions it returns connections. Could be more detailed about what 'connections' entail, but adequate for a read-only list operation.
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 no fluff. Essential information front-loaded with key instruction first, then detail on output format and purpose. Every sentence 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?
Given no output schema, description adequately explains what is returned and its format. Provides context on why this tool is necessary for other tools. Loses a point for not specifying whether the output includes metadata beyond names, but still sufficient.
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?
No parameters, schema coverage 100%. Description adds value by explaining the return format and usage of the output (platform names in kebab-case), which is helpful for subsequent tool invocation.
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?
Description clearly states 'List all available Pica integrations and platforms' with a specific verb and resource. It distinguishes itself from siblings (execute_pica_action, get_pica_action_knowledge, get_pica_platform_actions) by being the initial discovery tool.
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?
Explicitly says 'ALWAYS call this tool first in any workflow' and explains why (to discover platforms and connections needed for subsequent calls). Provides clear context for when to use vs alternatives.
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.
v1.0.0execute_pica_actionget_pica_action_knowledgeget_pica_platform_actionslist_pica_integrationsEach tool has a distinct purpose in a clearly defined workflow: listing integrations, fetching platform actions, retrieving action documentation, and executing the action. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern with snake_case (list_pica_integrations, get_pica_platform_actions, get_pica_action_knowledge, execute_pica_action). The verbs are action-oriented and match the tool's function.
With 4 tools, the server is lean but covers the essential steps for discovering and executing actions on third-party platforms. Each tool earns its place; the number is appropriate for the scope.
The toolset provides a complete workflow: discover available integrations, explore platform actions, get detailed action knowledge, and execute the action. There are no obvious gaps or dead ends for the stated purpose.
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