Cohere
Cohere provides enterprise language and multimodal AI models for chat, embeddings, reranking, classification, and related workflows.
Verdict
Common use cases
- Prototype RAG pipelines with document grounding
- Generate embeddings for semantic search indexing
- Run batch inference on thousands of prompts
- Test reasoning modes before production deployment
- Validate structured JSON output schemas
Integration
- Vendor
- Cohere
- Category
- other
- Auth
- API_KEY
- Tools
- 22
- Composio slug
cohere
Tools
- Cancel Batch
Cancel one in-progress Cohere batch. This requires a production Cohere API key, stops the batch irreversibly, and cannot be undone.
- Cancel Embed Job
Irreversibly cancel one active Cohere asynchronous embed job by ID. Cohere accrued charges for work processed before cancellation still apply, and partial results are not available. Cancel only a caller-owned job. This action sends one requ
- Chat
Generate one non-streaming Cohere chat response from an ordered conversation, optionally using documents, tools, structured output, or reasoning controls.
- Check API Key
Check whether the connected Cohere API key is valid and return its organization and owner identifiers when available.
- Create Batch
Start one asynchronous Cohere batch from an existing validated batch request dataset and return immediately without polling. Cohere requires a production API key for the Batch API.
- Create Dataset
Upload a Cohere dataset and start asynchronous validation. Returns immediately without waiting for validation to finish.
- Create Embed Job
Start one asynchronous Cohere embedding job for a validated embed-input dataset and return immediately without polling.
- Delete Datasetdestructive
Permanently delete a Cohere dataset by ID. This deletion cannot be undone.
- Detokenize Tokens
Convert ordered Cohere token IDs back to text using the tokenizer for an explicit matching model.
- Embed Inputs
Create v2 embeddings from exactly one input form: text strings, image data URIs, or structured multimodal content.
- Get Batch
Retrieve one asynchronous Cohere batch's status and counters by batch ID. A usable batch ID depends on a production-key CREATE_BATCH lifecycle; this action performs one read only, and callers control any bounded polling.
- Get Dataset
Retrieve one Cohere dataset by ID, including its asynchronous validation state, schema, files, parsing details, and metrics. Callers control any polling and should bound it.
- Get Dataset Usage
Read the organization's Cohere dataset-storage consumption in bytes.
- Get Embed Job
Retrieve one Cohere asynchronous embedding job's current status, model, and input and output dataset identifiers by job ID.
- Get Model
Retrieve a Cohere model's endpoint compatibility, context size, features, tokenizer, and default sampling parameters by model name.
- List Batches
Return one page of asynchronous Cohere batches and their statuses without creating or cancelling work.
- List Datasets
Return one offset-based page of Cohere datasets, optionally filtered by type, validation status, or creation time. Cohere provides no total or continuation metadata, so callers control offset explicitly.
- List Embed Jobs
List Cohere asynchronous embedding jobs and their current statuses.
- List Models
Discover Cohere model names and capabilities, optionally filtered to an endpoint or its default model. Returns one page without auto-pagination.
- Rerank Documents
Rank text documents by relevance to a query and return the highest-scoring source documents.
- Tokenize Text
Convert text into token IDs with an explicit Cohere model. Detokenization must use the same model because token IDs differ between model tokenizers.
- Transcribe Audio
Transcribe an uploaded audio file to text with a Cohere transcription model.
Setup
Setup guide
- 11. In Switchy, open Settings > Integrations and click Connect next to Cohere. 2. Paste your Cohere API key from the Cohere dashboard (dashboard.cohere.com under API Keys). 3. Click Authorize — Switchy validates the key and stores it encrypted. 4. Open any Project and type '@Cohere check API key' to confirm the connection returns your organization ID. 5. Test a real call with '@Cohere chat' followed by a prompt like 'Explain retrieval-augmented generation in one sentence.' 6. For batch or embed jobs, first upload a dataset using '@Cohere create dataset', wait for validation, then reference the dataset ID in subsequent batch or embed-job calls. 7. Production-tier keys unlock batch operations; trial keys work for chat and single-call embeddings only.
What teammates see: by default, memories from Cohere are scoped to the Project (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Projects have used this MCP together with a given model.
How Switchy teams use it
Starter prompts
Grounded Chat Response
@Cohere chat with the message 'Summarize the key risks' and include these documents as context: [paste your JSON array of document objects here].
Validate API Key Tier
@Cohere check API key and tell me the organization name and whether this is a production or trial key.
Create Embedding Dataset
@Cohere create dataset named 'product_descriptions_v2' with type 'embed-input' and upload the file at [your dataset URL or local path].
Start Batch Inference
@Cohere create batch using dataset ID 'ds_abc123' and return the batch job identifier so I can check progress later.
Cancel Running Job
@Cohere cancel embed job 'ej_xyz789' immediately and confirm the cancellation timestamp.
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
@cohere generate a chat response explaining why our Q4 revenue missed forecast, using the three financial reports I uploaded as context documents
Based on the three reports: Q4 revenue missed forecast by 8% primarily due to delayed enterprise renewals (contributing 5% of the gap) and higher-than-expected churn in the SMB segment (3%). The enterprise delay stemmed from extended procurement cycles at two anchor accounts. SMB churn correlates with the pricing change introduced in October. Mitigation recommendations include accelerating the enterprise sales cycle and revisiting SMB pricing tiers.
This example uses the Chat tool with document grounding, letting Cohere synthesize insights from uploaded files. The response quality depends on document relevance and prompt clarity. Be aware: each chat call consumes tokens proportional to document length, so large PDFs can hit rate limits quickly.
Use-case deep-dives
When Cohere wins for semantic search at scale
A 6-person support team needs to search 40,000 help articles and past tickets by meaning, not keywords. Cohere's embed job tools let you batch-process the entire corpus overnight without blocking your workflow. You upload the dataset, kick off the job, and check back in the morning—no streaming timeouts, no rate-limit babysitting. The trade-off: if your knowledge base changes hourly, the async model adds latency you might not tolerate. But for weekly or monthly refreshes, Cohere's batch embedding is the right call. You get production-grade semantic search without writing your own chunking pipeline.
When Cohere's chat tool beats a generic LLM wrapper
A 3-person product team is prototyping a chatbot that references internal docs and calls external APIs mid-conversation. Cohere's chat tool supports documents, tool-calling, and structured output in one call—no prompt-engineering gymnastics to coax JSON from a base model. You pass the conversation history, attach the relevant docs, and get a response that can trigger your Slack or Jira tool. The boundary: if you need streaming for a user-facing UI, Cohere's non-streaming chat won't cut it. But for backend prototypes or Slack bots where 2-second latency is fine, this MCP saves you from duct-taping three SDKs together.
When batch cancellation matters for budget guardrails
A 10-person data team runs weekly batch jobs to classify 500,000 support tickets. Halfway through a run, they spot a config error that will waste $200 in API credits. Cohere's cancel-batch tool stops the job immediately—no waiting for a support ticket, no burning the rest of the budget. The catch: cancellation is irreversible and you lose partial results, so you need monitoring that catches errors early. If your team runs unattended batch jobs without mid-flight checks, this safety net won't help. But for teams with a human in the loop during long runs, Cohere's batch controls turn a budget disaster into a 5-minute fix.
Frequently asked
What does the Cohere MCP do in Switchy?
It connects your team to Cohere's language models for chat completions, embeddings, and batch processing. You can generate responses with retrieval-augmented generation, run async embed jobs on large datasets, and manage production batch workflows. All 22 tools require a valid Cohere API key, which you provide once during setup.
Do I need a production Cohere API key for all features?
No. Chat and single-embed calls work with trial keys. Batch creation, batch cancellation, and async embed jobs require a production key — Cohere enforces this at the API level. If you try to create a batch with a trial key, the tool will fail with a 403 error.
Can the Cohere MCP fine-tune models or train custom embeddings?
No. This MCP covers inference only: chat completions, embeddings, and batch jobs. Fine-tuning and custom model training happen in Cohere's dashboard. Once you've trained a model there, you can call it by ID through the Chat tool in Switchy.
Why use this MCP instead of calling Cohere's API directly?
The MCP wraps Cohere's API in a shared workspace where your team sees the same context, dataset IDs, and batch statuses. You avoid writing auth boilerplate or polling logic for async jobs. If you already have a working Python client, stick with that.
Who on the team should connect the Cohere API key?
Whoever owns your Cohere account and can generate production keys. Trial keys let anyone experiment with chat, but batch and embed jobs need production access. One key per workspace is enough — Switchy doesn't track per-user quotas, so Cohere bills roll up to the connected account.