database
Pinecone
Connect Pinecone to Elaichi and hand every teammate a governed MCP endpoint — 47 tools ready to call from Claude, Cursor, or any MCP client, clamped by roles, restrictions, and an audit log.
AI tools
Pinecone tools for your AI agents
47 tools are ready to mint as an MCP endpoint the moment you connect Pinecone — governed by the same roles, restrictions, and audit log as everything else in Elaichi.
get_single_pinecone_index_by_id
Get a specific Pinecone index by id. Returns the full index object including name, host, metric, dimension, spec, deletion_protection, and status. Required: id.
create_a_pinecone_index
Create a new Pinecone index for dense vectors, sparse vectors, or full-text search documents. Returns the created index object including name, host, and status. Required: name and spec (with cloud and region). Dense vector indexes also require dimension and metric; sparse vector indexes require metric set to dotproduct.
list_all_pinecone_indexes
List all Pinecone indexes in the current project. Returns an array of index objects including name, dimension, metric, host, and status for each.
update_a_pinecone_index_by_id
Update configuration for a Pinecone index by id. Supports updating deletion_protection and tags. Required: id.
delete_a_pinecone_index_by_id
Delete a Pinecone index by id. Returns an empty response on success. Required: id.
pinecone_indexes_create_for_model
Create a Pinecone index with integrated embedding that automatically converts source text to dense or sparse vectors using a hosted model. Returns the created index object. Required: name, cloud, region, and embed (with model and field_map).
pinecone_indexes_describe_index_stats
Get statistics for a Pinecone index including dimension, index fullness, total vector count, and per-namespace vector counts. Required: index_host.
update_a_pinecone_vector_by_id
Update a Pinecone vector's dense values and/or metadata by id. Returns an empty response on success.
delete_a_pinecone_vector_by_id
Delete Pinecone vectors by id list, metadata filter, or by deleting all vectors in a namespace. Returns an empty response on success. Provide at least one of ids, filter, or deleteAll.
pinecone_vectors_bulk_create
Upsert (insert or update) vectors into a Pinecone index namespace. Existing records with the same id are fully overwritten. Returns upsertedCount. Required: vectors.
get_single_pinecone_vector_by_id
Fetch Pinecone vectors by their ids. Returns vector objects including id, values, and metadata. Required: ids.
list_all_pinecone_vectors
List vector ids in a Pinecone index namespace. Returns vector id objects. Optionally filter by id prefix.
pinecone_vectors_search
Query a Pinecone index for the most similar vectors to a given query vector or vector id. Returns matches including id, score, values, and metadata. Required: topK.
create_a_pinecone_import
Start an asynchronous bulk import of vectors from object storage (Amazon S3, Google Cloud Storage, or Azure Blob Storage) into a Pinecone serverless index. Returns the import `id` for tracking progress. Required: `uri`.
delete_a_pinecone_import_by_id
Cancel a Pinecone bulk import operation by id if it has not yet finished. Has no effect if the import is already complete. Returns an empty response on success.
get_single_pinecone_import_by_id
Get details of a specific Pinecone bulk import by id. Returns: id, uri, status, percent_complete, records_imported, created_at, finished_at, and an error message if the import failed.
list_all_pinecone_imports
List all recent and ongoing Pinecone bulk imports for an index. Returns an array of import objects each including id, uri, status, percent_complete, and records_imported.
create_a_pinecone_collection
Create a Pinecone collection (a static backup of a pod-based index). Returns the created collection object including its name. Required: name, source. Collections are a pod-only feature and are not supported for serverless indexes.
list_all_pinecone_collections
List all Pinecone collections in the current project. Returns: name, status, environment, size, vector_count, and dimension for each collection. Serverless indexes do not support collections.
get_single_pinecone_collection_by_id
Get details about a specific collection in Pinecone. Requires id. Returns name, status, environment, size, vector_count, and dimension of the collection. Serverless indexes do not support collections.
delete_a_pinecone_collection_by_id
Delete a collection in Pinecone. Requires id as collection_name. Returns a 202 status on successful deletion. Serverless indexes do not support collections.
delete_a_pinecone_record_by_id
Delete Pinecone records from a namespace by record IDs, a metadata filter expression, or delete all records at once. Returns an empty response on success. Required: namespace. Max 1000 IDs per request when deleting by ID.
list_all_pinecone_records
List record IDs stored in a Pinecone namespace. Returns an array of record id values. Required: namespace.
pinecone_records_search
Search a Pinecone namespace for semantically similar records using a query text or vector. Returns hits including _id, _score, and requested fields. Required: namespace, query (with top_k and inputs).
create_a_pinecone_record
Upsert records into a Pinecone namespace using NDJSON format, inserting new records or updating existing ones matched by id. Required: namespace.
create_a_pinecone_backup
Create a backup of a Pinecone index. Returns the new backup object including backup_id, status, source_index_name, cloud, region, and created_at. Required: index_name.
delete_a_pinecone_backup_by_id
Delete a Pinecone backup by id. Returns an empty response on success. Required: id.
get_single_pinecone_backup_by_id
Get the description of a Pinecone backup by id. Returns: backup_id, source_index_name, status, name, description, cloud, region, dimension, record_count, and created_at. Required: id.
list_all_pinecone_backups
List all Pinecone backups for a project. Returns an array of backup objects each including backup_id, source_index_name, status, name, cloud, region, and created_at.
pinecone_backups_index
Create an index from a specific backup in Pinecone. Requires backup_id. Returns restore_job_id (the created restore job’s identifier) and index_id (the newly created index identifier).
get_single_pinecone_restore_job_by_id
Get a description of a Pinecone restore job by id. Returns: restore_job_id, backup_id, target_index_name, status, percent_complete, created_at, completed_at. Required: id.
list_all_pinecone_restore_jobs
List all Pinecone restore jobs for a project. Returns an array of restore job objects including restore_job_id, backup_id, target_index_name, status, and percent_complete.
list_all_pinecone_index_backups
List all backups for a Pinecone index. Returns: backup_id, source_index_name, status, cloud, region, created_at. Required: index_name.
create_a_pinecone_namespace
Create a namespace in a Pinecone serverless index. Returns the created namespace including name, record_count, and schema. Required: name. Not supported for pod-based indexes.
delete_a_pinecone_namespace_by_id
Delete a namespace from a Pinecone serverless index by id. Permanently removes all data in the namespace — this action is irreversible. Returns an empty response on success. Required: id. Not supported for pod-based indexes.
get_single_pinecone_namespace_by_id
Get details of a Pinecone namespace by id. Returns: name, record_count. Required: id. Not supported for pod-based indexes.
list_all_pinecone_namespaces
List all namespaces in a Pinecone serverless index. Returns each namespace's name and record_count. Not supported for pod-based indexes.
get_single_pinecone_model_by_id
Get a description of a specific Pinecone hosted model by id. Returns: model name, type, vector_type, default_dimension, modality, provider_name, and supported_parameters. Required: id.
list_all_pinecone_models
List all embedding and reranking models hosted by Pinecone. Returns an array of models, each including model name, type, vector_type, default_dimension, modality, provider_name, and supported_parameters.
create_a_pinecone_embedding
Generate vector embeddings for input data using Pinecone's hosted embedding models. Returns: data (array of embedding value arrays), model name, and usage (total_tokens). Required: model and inputs.
create_a_pinecone_rerank
Rerank a list of documents by relevance to a query using Pinecone's inference API. Returns a ranked `data` array of results each containing `index`, `document`, and `score`, plus `usage.rerank_units`. Required: `model`, `query`, and `documents`.
list_all_pinecone_vector_records
List vector records from a Pinecone index by fetching vectors matched against metadata. Returns a collection of vectors from the specified index. Required: index_host.
list_all_pinecone_backup_schedules
List all backup_schedules for a specific index in Pinecone. Requires index_name. Returns schedule_id, name, index_id, project_id, schedule_type, frequency, retention_expire_after_days, enabled, next_scheduled_run, and created_at in the response.
get_single_pinecone_backup_schedule_by_id
Get details of a specific backup schedule in Pinecone. Requires id. Returns schedule_id, name, index_id, project_id, schedule_type, frequency, retention_expire_after_days, enabled, next_scheduled_run, and created_at.
update_a_pinecone_backup_schedule_by_id
Update a backup schedule in Pinecone. Requires id. Returns schedule_id, name, index_id, project_id, schedule_type, frequency, retention_expire_after_days, enabled, next_scheduled_run, and created_at in the response.
delete_a_pinecone_backup_schedule_by_id
Delete a backup schedule in Pinecone using id. This operation removes the schedule but does not delete backups created by it. Returns a 204 status code upon successful deletion.
list_all_pinecone_backup_schedule_history
List backup schedule history in Pinecone for a specific schedule_id. Returns details including backup_id, status, scheduled_execution_at, cloud, region, and created_at for each backup entry.
How it works
From Pinecone account to governed MCP endpoint
Connect
Link a Pinecone account through Elaichi's hosted connect flow. Credentials are vaulted — nobody, including the AI, ever sees them.
Compose
The connection becomes a toolbox instantly. Curate which Pinecone tools are exposed, rename them, or freeze arguments.
Mint
Mint a personal MCP endpoint on that toolbox and paste it into Claude, Cursor, or any MCP client — governed by roles, restrictions, and audit logs.
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