Artificial Intelligence
Cohere
API integration
Ship Artificial Intelligence features without building the integration. Full Cohere API access via Proxy and 40+ MCP-ready tools for AI agents — extend models and mappings to fit your product.
Talk to usUse Cases
Why integrate with Cohere
Common scenarios for SaaS companies building Cohere integrations for their customers.
Ship AI features without hosting your own models
Give your customers grounded chat, semantic search, and classification powered by Cohere's Command and Embed models, while Truto handles authentication and request lifecycle so your team focuses on the product experience.
Let customers bring their own Cohere account
Enterprise buyers often have existing Cohere contracts and data residency requirements. Truto lets end users connect their own Cohere API keys so usage, billing, and compliance stay under their control.
Power multi-tenant RAG pipelines
Run per-customer embed jobs and rerank calls through a single integration layer, so each tenant's data stays isolated and you can scale ingestion without building bespoke job orchestration.
Add audit-ready generative AI to regulated workflows
Cohere's Chat API returns citations and response IDs — ideal for LegalTech, FinTech, and healthcare SaaS that need to prove why the model produced a given answer.
Consolidate AI vendor management for your customers
Offer Cohere alongside other AI providers in your product with a consistent connection experience, letting customers choose the model provider that fits their policies and pricing.
What You Can Build
Ship these features with Truto + Cohere
Concrete product features your team can ship faster by leveraging Truto’s Cohere integration instead of building from scratch.
Grounded in-app chatbot with citations
Use the Chat API to answer end-user questions against their own knowledge base and render Cohere's citation array as clickable source references in your UI.
AI-powered semantic search upgrade
Wrap your existing keyword search with the Rerank endpoint to reorder up to 1,000 candidate documents by true semantic relevance without migrating your database.
Bulk document embedding pipeline
Kick off asynchronous embed jobs on customer datasets, poll job status, and stream vectors into your search index once complete — all through a consistent integration surface.
Zero-shot ticket and content classification
Route support tickets, tag content, or score sentiment using the Classify endpoint with a handful of labeled examples, no model training required.
Call recording insights generator
Transcribe customer calls via the Audio Transcription endpoint and pipe the text into Chat to extract action items, summaries, or risk signals.
Enterprise connector-backed answers
Let customers register and OAuth-authorize Cohere Connectors so chat responses can pull live data from their Jira, Slack, or Drive without you building each scraper.
SuperAI
Cohere AI agent tools
Comprehensive AI agent toolset with fine-grained control. Integrates with MCP clients like Cursor and Claude, or frameworks like LangChain.
create_a_cohere_chat
Generate a text response from a Cohere model given a chronological list of chat messages. Returns: id, finish_reason, message, usage, logprobs. Required: model, messages, stream.
create_a_cohere_chat_v_1
Generate a text response to a user message in Cohere. Returns: text, generation_id, response_id, citations, finish_reason. Required: message, stream.
create_a_cohere_prompt
Construct the text prompt that Cohere would send to the model for a v2 chat request without running generation. Returns: prompt. Required: model, messages. The request body matches Chat v2; the stream field is ignored.
create_a_cohere_rerank
Rerank a list of documents against a search query in Cohere, producing an ordered array with each text assigned a relevance score. Returns: results, id, meta. Required: model, query, documents. Up to 1,000 documents recommended per request; longer documents are auto-truncated to max_tokens_per_doc.
create_a_cohere_embed
Generate text and image embeddings in Cohere using the v2 Embed API. Returns: id, embeddings, texts, images, meta. Required: model, input_type. Maximum 96 texts per call; image embeddings supported with Embed v3.0 and newer.
create_a_cohere_embed_job
Create an async embed job in Cohere for a Dataset of type embed-input. The completed job produces a new Dataset of type embed-output with the original text entries and corresponding embeddings. Returns: job_id, meta. Required: model, dataset_id, input_type.
list_all_cohere_embed_jobs
List all embed jobs in Cohere for the authenticated user. Returns each embed job record including job_id, status, created_at, input_dataset_id, model, name, output_dataset_id, truncate, and meta.
get_single_cohere_embed_job_by_id
Get a single embed job by id in Cohere. Returns: job_id, status, created_at, input_dataset_id, model, truncate, name, output_dataset_id, meta. Required: id.
cohere_embed_jobs_cancel
Cancel an active embed job in Cohere. Users are charged for embeddings processed up to the cancellation point and partial results are not available after cancellation. Returns an empty 200 response on success. Required: embed_job_id.
create_a_cohere_audio_transcription
Transcribe an audio file in Cohere. Returns the transcribed text. Required: model, language, file.
create_a_cohere_batch
Create and execute a batch in Cohere from an uploaded dataset of requests. Returns the batch object including its id, name, input_dataset_id, model, created_at, and updated_at. Required: name, input_dataset_id, model.
list_all_cohere_batches
List batches for the current authenticated user in Cohere. Returns each batch with its id, name, input_dataset_id, model, created_at, and updated_at. Max 1000 per page.
get_single_cohere_batch_by_id
Get a single batch by id in Cohere. Returns the batch object including its id, name, input_dataset_id, model, created_at, and updated_at. Required: id.
cohere_batches_cancel
Cancel an in-progress batch in Cohere by batch_id. Returns an empty 204 response on success. Required: batch_id.
create_a_cohere_dataset
Create a Cohere dataset by uploading a file via multipart form. Returns: id, name, dataset_type, validation_status, created_at, warnings. Required: name, type, data. The only valid type is embed-input.
list_all_cohere_datasets
List Cohere datasets with optional filtering by type, validation status, and date range. Returns: id, name, dataset_type, validation_status, created_at, warnings.
get_single_cohere_dataset_by_id
Get a single Cohere dataset by id. Returns: id, name, dataset_type, validation_status, created_at, warnings. Required: id.
delete_a_cohere_dataset_by_id
Delete a Cohere dataset by id. Returns an empty 204 response on success. Required: id.
cohere_datasets_get_usage
View the dataset storage usage for your Cohere organization. Returns: organization_usage. Each organization can have up to 10GB of storage across all users.
create_a_cohere_tokenize
Tokenize input text in Cohere using byte-pair encoding (BPE). Returns: tokens, token_strings, meta. Required: text, model. Text length must be between 1 and 65536 characters.
create_a_cohere_detokenize
Detokenize a list of byte-pair encoding tokens back into their text representation in Cohere. Returns: text, meta. Required: tokens, model.
list_all_cohere_models
List models available for use in Cohere. Returns each model's name, endpoints, finetuned, context_length, is_deprecated, tokenizer_url, default_endpoints, features, and sampling_defaults. Optional filters: endpoint (filter by compatible API endpoint) and default_only (only default models for the specified endpoint, requires endpoint).
get_single_cohere_model_by_id
Get details of a single Cohere model by its name (id). Returns: name, is_deprecated, endpoints, finetuned, context_length, tokenizer_url, default_endpoints, features, sampling_defaults. Required: id.
create_a_cohere_classify
Classify up to 96 text inputs in Cohere by predicting the best-fitting label for each input using provided text+label example pairs as reference. Returns: id, classifications, meta. Required: inputs. Fine-tuned models do not require the examples parameter.
create_a_cohere_generate
Generate realistic text from a given prompt using Cohere's legacy Generate API. Returns: id, generations (list of generated results), prompt, and meta. Required: prompt, stream. This API is legacy and no longer maintained; migrate to the Chat API.
create_a_cohere_summarize
Generate a summary in English for a given text using the Cohere Summarize API (Legacy). Returns: id, summary, meta. Required: text. Input text must be 250–50,000 characters and currently only English is supported.
cohere_api_keys_check
Check that a Cohere API key is valid and active. Returns: valid, organization_id, owner_id.
list_all_cohere_connectors
List Cohere connectors ordered by descending creation date (newer first). Returns: id, name, url, description, excludes, oauth, active, continue_on_failure, service_auth. Max 100 per page.
create_a_cohere_connector
Create a new Cohere connector. The connector is tested during registration and registration is cancelled if the test fails. Returns: id, name, url, description, excludes, oauth, active, continue_on_failure, service_auth. Required: name, url.
get_single_cohere_connector_by_id
Get a single Cohere connector by id. Returns: id, name, url, description, excludes, oauth, active, continue_on_failure, service_auth. Required: id.
update_a_cohere_connector_by_id
Update a Cohere connector by id. Omitted fields are not updated. Returns: id, name, url, description, excludes, oauth, active, continue_on_failure, service_auth. Required: id.
delete_a_cohere_connector_by_id
Delete a Cohere connector by id. Returns an empty 204 response on success. Required: id.
cohere_connectors_oauth_authorize
Authorize a Cohere connector via OAuth 2.0 for the connector OAuth app. Returns: redirect_url. Required: connector_id.
list_all_cohere_finetuned_models
List fine-tuned models in Cohere that the caller has access to. Returns each model with id, name, status, settings, and created_at. Supports sorting by created_at.
create_a_cohere_finetuned_model
Create a new fine-tuned model in Cohere with a name and training settings (dataset, hyperparameters). Training runs asynchronously. Returns the model with id, name, status, settings, and created_at. Required: name, settings.
get_single_cohere_finetuned_model_by_id
Get a single fine-tuned model in Cohere by id. Returns the model with id, name, status, settings, and created_at. Required: id.
update_a_cohere_finetuned_model_by_id
Update a fine-tuned model in Cohere by id with a new name and settings. Returns the updated model with id, name, status, settings, and created_at. Required: id, name, settings.
delete_a_cohere_finetuned_model_by_id
Delete a fine-tuned model in Cohere by id. This operation is irreversible. Returns an empty 204 response on success. Required: id.
list_all_cohere_finetuned_model_events
List events that occurred during the life-cycle of a Cohere fine-tuned model, ordered by creation time with the most recent first. Returns: created_at. Required: finetuned_model_id.
list_all_cohere_finetuned_model_metrics
List training step metrics for a Cohere fine-tuned model, ordered by step number with the most recent step first. Returns a list of step metric objects containing training evaluation data for each step. Required: finetuned_model_id.
Why Truto
Why use Truto’s MCP server for Cohere
Other MCP servers give you a static tool list for one app. Truto gives you a managed, multi-tenant MCP infrastructure across 600+ integrations.
Auto-generated, always up to date
Tools are dynamically generated from curated documentation — not hand-coded. As integrations evolve, tools stay current without manual maintenance.
Fine-grained access control
Scope each MCP server to read-only, write-only, specific methods, or tagged tool groups. Expose only what your AI agent needs — nothing more.
Multi-tenant by design
Each MCP server is scoped to a single connected account with its own credentials. The URL itself is the auth token — no shared secrets, no credential leaking across tenants.
Works with every MCP client
Standard JSON-RPC 2.0 protocol. Paste the URL into Claude, ChatGPT, Cursor, or any MCP-compatible agent framework — tools are discovered automatically.
Built-in auth, rate limits, and error handling
Tool calls execute through Truto’s proxy layer with automatic OAuth refresh, rate-limit handling, and normalized error responses. No raw API plumbing in your agent.
Expiring and auditable servers
Create time-limited MCP servers for contractors or automated workflows. Optional dual-auth requires both the URL and a Truto API token for high-security environments.
How It Works
From zero to integrated
Go live with Cohere in under an hour. No boilerplate, no maintenance burden.
Link your customer’s Cohere account
Use Truto’s frontend SDK to connect your customer’s Cohere account. We handle all OAuth and API key flows — you don’t need to create the OAuth app.
We handle authentication
Don’t spend time refreshing access tokens or figuring out secure storage. We handle it and inject credentials into every API request.
Call our API, we call Cohere
Truto’s Proxy API is a 1-to-1 mapping of the Cohere API. You call us, we call Cohere, and pass the response back in the same cycle.
Unified response format
Every response follows a single format across all integrations. We translate Cohere’s pagination into unified cursor-based pagination. Data is always in the result attribute.
FAQs
Common questions about Cohere on Truto
Authentication, rate limits, data freshness, and everything else you need to know before you integrate.
How do end users authenticate their Cohere account?
End users provide a Cohere API key, which Truto stores securely and injects into every request. You can validate a key at connection time using the API keys check endpoint before allowing further calls.
Which Cohere endpoints does Truto support?
Truto exposes Cohere's Chat (v1 and v2), Rerank, Embed, Embed Jobs, Classify, Generate, Summarize, Tokenize/Detokenize, Datasets, Batches, Audio Transcription, Models, Fine-tuned Models, and Connectors endpoints as first-class tools.
Can I run long-running embedding jobs without hitting timeouts?
Yes. Use the async Embed Jobs flow: create a dataset, start an embed job, then poll job status via the get-by-id endpoint or cancel it if needed. This avoids synchronous timeouts on large corpora.
How do I handle rate limits and quotas?
Rate limits are enforced by Cohere per API key and vary by endpoint and account tier. Truto passes through Cohere's rate limit responses so you can implement retries and backoff in your integration logic.
Can customers use their own fine-tuned models?
Yes. Truto supports listing, creating, updating, and deleting fine-tuned models, as well as fetching training events and metrics, so customers can manage custom models tied to their own Cohere account.
How do I enable Cohere Connectors for RAG?
Use the Connectors endpoints to create and manage connector definitions, and trigger the OAuth authorize flow so end users can grant Cohere access to their external tools. Connectors can then be referenced in Chat calls to ground responses in live data.
Cohere
Get Cohere integrated into your app
Our team understands what it takes to make a Cohere integration successful. A short, crisp 30 minute call with folks who understand the problem.