Griptape
Griptape is a comprehensive platform offering tools and frameworks for building, deploying, and scaling generative AI applications.
Verdict
Common use cases
- Trigger multi-step AI pipelines from chat
- Monitor long-running assistant jobs in real time
- Retrieve and share completed run results
- Cancel stuck or runaway assistant executions
- Deploy new tools from Git repos on demand
Integration
- Vendor
- Griptape
- Category
- other
- Auth
- API_KEY
- Tools
- 17
- Composio slug
griptape
Tools
- Assistant Creation
Tool to create a new assistant. Use when you need to register a uniquely named assistant in Griptape Cloud before initiating runs.
- Assistant Run Creation
Tool to initiate a new assistant run. Use when starting a fresh execution with custom arguments or resources after selecting the assistant.
- Assistant Run Logs Retrieval
Tool to retrieve logs generated during the execution of a specific assistant run. Use after confirming the run_id to fetch all log events once the run has completed.
- Assistant Run Result Retrieval
Tool to fetch the final result of a completed assistant run. Use after confirming the run has finished to retrieve its final assistant-generated output.
- Cancel Assistant Run
Tool to cancel an ongoing assistant run. Use when you need to stop a run prematurely after confirming the run ID is valid.
- Create Tool
Tool to create a new tool in Griptape Cloud. Use when you need to deploy a tool from a Git repo or ZIP archive.
- Get Assistant Run Error Details
Tool to fetch detailed error information for a specific assistant run. Use after an assistant run has failed to inspect error details.
- Get Ruleset by Alias
Tool to retrieve a ruleset by its alias. Use after confirming the alias to fetch the ruleset's details from Griptape Cloud.
- Get Tool Deployment Status
Tool to retrieve status of a specific tool deployment. Use after creating a deployment to poll its progress.
- List Assistant Runs
Tool to list all runs for a given assistant. Use when you need historical run executions for analysis after using the assistant.
- List Assistants
Tool to list all assistants. Use after authenticating with Griptape Cloud to retrieve available assistants. Supports optional pagination.
- List Embedding Drivers
Tool to list available embedding drivers. Use when you need to retrieve supported drivers and their models before embedding text.
- List Tools
Tool to list all tools. Use when you need to discover available tools and their IDs in Griptape Cloud.
- Retrieve Assistant Run
Tool to retrieve an assistant run's status and details. Use when you need current status and metadata for a specific assistant run.
- Retry Assistant Run
Tool to retry a previously failed assistant run. Use when a run has failed and you need to rerun it with the same parameters.
- Ruleset Creation
Tool to create a new ruleset. Use when you need to define a new set of rules with a unique alias before executing runs.
- Stream Assistant Run Events
Tool to stream real-time events for a specific Assistant run. Use after initiating a run to monitor its progress.
Setup
Setup guide
- 11. Open your Switchy workspace and navigate to Settings > Integrations > MCP Servers. 2. Click Add MCP Server and select Griptape from the catalog. 3. Choose API Key as the authentication method. 4. Log into Griptape Cloud, navigate to your account settings, and generate a new API key with assistant and run management permissions. 5. Paste the key into Switchy and click Connect. 6. Switchy will verify the key and confirm the connection. 7. Open any Space and type '@Griptape list my assistants' to test — you should see a response listing your configured assistants. 8. To trigger a run, use '@Griptape start a run for assistant [name] with arguments [JSON]' and the MCP will return a run ID you can reference later.
What teammates see: by default, memories from Griptape are scoped to the Space (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Spaces have used this MCP together with a given model.
How Switchy teams use it
Starter prompts
List Active Assistants
@Griptape show me all assistants currently configured in my Cloud accountOpen in a Space →
Start Pipeline Run
@Griptape create a new run for the 'data-enrichment' assistant with input JSON {"source": "crm_export.csv"}Open in a Space →Check Run Status
@Griptape get the current status and logs for run ID abc123Open in a Space →
Retrieve Final Output
@Griptape fetch the final result for run ID abc123 and summarize itOpen in a Space →
Cancel Runaway Job
@Griptape cancel the assistant run with ID abc123 immediatelyOpen in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
@Griptape create an assistant named 'weekly-reporter' that summarizes our team's activity and list all available assistants
I've created the assistant 'weekly-reporter' in your Griptape Cloud workspace. The assistant is now registered and ready to accept runs. Your current assistants include: weekly-reporter (just created), data-pipeline-monitor (last run 2 days ago), and customer-feedback-analyzer (last run 5 hours ago). To start a run with weekly-reporter, you'll need to provide any required arguments or resources specific to your team activity data sources.
This example shows assistant creation and listing. You'll need a valid Griptape Cloud API key with write permissions. Each assistant requires a unique name within your workspace. The MCP doesn't automatically configure data sources — you must specify those when initiating runs.
@Griptape start a run of my 'data-pipeline-monitor' assistant with the argument repo_name='analytics-etl' and fetch the result when complete
I've initiated run #847 for your data-pipeline-monitor assistant with repo_name='analytics-etl'. The run completed in 23 seconds. Result: Pipeline health check passed. Last successful ETL: 2024-01-15 14:32 UTC. 3 tables updated (users, events, sessions). No schema drift detected. Warning: events table row count increased 340% vs. 7-day average — verify data source integrity.
This demonstrates the full run lifecycle: creation, monitoring, and result retrieval. The MCP polls run status internally, so response time depends on your assistant's execution duration. If a run exceeds 5 minutes, you may hit MCP timeout limits — consider async patterns for long-running workflows.
@Griptape retrieve logs for run #823 of my 'customer-feedback-analyzer' assistant and explain any errors you find
Run #823 generated 47 log events over 18 seconds. The run failed at the sentiment analysis stage. Error details: RateLimitExceeded from the underlying NLP API (429 status). The assistant successfully ingested 340 feedback entries and completed keyword extraction, but hit the rate limit when processing batch 4 of 7. Recommendation: Configure rate-limit backoff in your assistant's ruleset or reduce batch size to stay within your NLP provider's quota.
This example pairs log retrieval with AI analysis to diagnose failures. The MCP surfaces structured error details that the AI can interpret. Useful for debugging assistant runs without leaving your Space. Note that log retention policies vary by Griptape Cloud plan — older runs may have truncated logs.
Use-case deep-dives
When Griptape wins for building custom AI agent pipelines
A 6-person engineering team building a customer onboarding automation needs multiple AI agents to handle document parsing, data validation, and CRM updates in sequence. Griptape is the right call here because the 17 tools let you create, run, and monitor discrete assistants that hand off work to each other. You define each assistant with its own ruleset, kick off runs with custom arguments, and pull logs or results when each step finishes. The API-key auth keeps setup simple for internal tooling. The threshold: if your workflow is a single-shot task with no handoffs, you're overbuilding—Griptape shines when you need orchestration, not one-off completions. If your team already runs agent pipelines in production and needs observability across runs, this MCP gives you the control plane without writing your own polling logic.
When this MCP is essential for post-mortem analysis
A 3-person ops team runs nightly data-enrichment agents that occasionally fail on malformed inputs. Griptape's error-detail and log-retrieval tools let them pull the exact failure point and stack trace from Griptape Cloud without SSH-ing into containers or tailing logs manually. After a run fails, they fetch error details by run ID, inspect the log events to see which tool call broke, and adjust the ruleset or input schema. This works best when your agents are already deployed in Griptape Cloud and you need forensic access during incidents. If your agents run locally or you're not using Griptape's hosted platform, these tools won't help—you'd need direct filesystem or container access instead. For teams running Griptape-managed agents at any scale, this MCP turns debugging from a scavenger hunt into a two-minute lookup.
When this MCP accelerates CI/CD for AI tooling
A 5-person product team ships new AI capabilities every sprint by pushing tool definitions to a Git repo, then deploying them to Griptape Cloud via the Create Tool endpoint. Instead of manually uploading ZIP files or clicking through a UI, they script the deployment in their CI pipeline: merge to main triggers a GitHub Action that calls the MCP to register the new tool, then kicks off a test run to validate it. This pattern works when your team treats AI tools like any other service—versioned, tested, deployed. The trade-off: if you're prototyping one-off tools or your team doesn't have a Git-based workflow, the overhead isn't worth it. But for teams shipping AI features continuously and already using Griptape Cloud, this MCP turns tool deployment into a single API call instead of a manual release checklist.
Frequently asked
What does the Griptape MCP do in Switchy?
It connects Switchy to Griptape Cloud, letting your team create and run AI assistants, manage tools, and retrieve execution logs without leaving the workspace. You can start assistant runs with custom arguments, cancel them mid-execution, and fetch results or error details once they complete. Think of it as orchestrating Griptape's agent framework directly from Switchy's interface.
Do I need a Griptape Cloud API key to use this MCP?
Yes. The MCP authenticates via API key, so you'll need a valid Griptape Cloud account and an active key with permissions to create assistants, initiate runs, and access logs. Without it, none of the 17 tools will function. Grab your key from Griptape's dashboard before connecting.
Can the Griptape MCP edit or delete existing assistants?
No. The toolset focuses on creation and execution: you can create new assistants, start runs, cancel them, and retrieve logs or results. If you need to modify or delete an assistant, do that directly in Griptape Cloud's UI. The MCP doesn't expose update or delete operations for assistants.
How is this different from calling Griptape's API directly?
The MCP wraps Griptape's API into Switchy's shared workspace, so your team doesn't write code or manage credentials individually. You get a conversational interface to trigger runs, check logs, and handle errors—no curl commands, no SDK setup. If you're already scripting Griptape workflows, the MCP won't add much; if you're coordinating a team, it centralises access.
Who on the team should connect the Griptape MCP?
Whoever owns your Griptape Cloud account and has admin access to generate API keys. Once connected, any Switchy member can invoke the tools—creating assistants, starting runs, fetching logs—so treat the connection like a shared service account. If multiple people need isolated Griptape projects, connect separate MCPs with different keys.