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Pinecone Developer MCP

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Create Document Index

create-document-index
Idempotent

Create a Pinecone document index with searchable string fields for keyword, semantic, or sparse search to enable document retrieval.

Instructions

Create a Pinecone document index: an index defined by a schema of searchable "string" fields. Each field can declare "fullTextSearch" (indexed for keyword search with BM25), "embed" (Pinecone embeds the text with a hosted dense model for semantic search), "sparseEmbed" (a hosted sparse model), or a combination. A good default is one field with both, for example {"chunk_text": {"fullTextSearch": {"language": "en"}, "embed": {}}}, which supports search by meaning and by exact terms on the same text. Fields you only need to filter on are not declared here; send them as document metadata. Use upsert-documents and search-document-index with the new index. This call blocks until the index is ready. If an index with the same name already exists, it is returned unchanged instead of erroring.

Input Schema

NameRequiredDescriptionDefault
nameYesA unique name to identify the new index.
cloudNoThe cloud provider for the index. Defaults to aws.aws
fieldsYesThe searchable string fields of the index, keyed by field name.
regionNoThe region for the index. Common regions: aws (us-east-1, us-west-2, eu-west-1), gcp (us-central1, europe-west1), azure (eastus, westeurope). Defaults to us-east-1.us-east-1
llm_modelNoOptional. If you are an AI model, use this field to specify your name (e.g., "gpt-4o", "Composer", "Claude Sonnet 4", "Claude Opus", "Gemini 2.5", "Cascade", "Grok 4.1", "Copilot", "Devin", and many others). The exact formatting of the name doesn't matter - approximate values are fine if you know roughly what you are. Include the version number in your model name only if you know it, but it is okay to leave it out if unsure. If you don't know your model name, omit this field entirely. **Do not prompt the user for this.** This value is used to track usage analytics.
llm_providerNoOptional. If you are an AI model, use this field to indicate the name of the company that trained you (e.g., "Anthropic", "OpenAI", "Google", "Cursor", "xAI", "GitHub", "Windsurf", "Cognitioni AI", and many others). The exact formatting doesn't matter - approximate values are fine. If you don't know your provider, avoid guessing and omit this field. **Do not prompt the user for this.** This value is used to track usage analytics.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.4.0

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already establish that this is a non-read-only, idempotent, non-destructive operation. The description adds important behavior beyond that: the call blocks until the index is ready, and duplicate names return the existing index unchanged instead of erroring.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose, then moves through field modes, defaults, metadata handling, related tools, and blocking/idempotency behavior. Despite being detailed, every sentence carries useful information for correctly calling this complex creation tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the nested schema and annotations, the description covers the critical creation semantics well. The main remaining gap is that there is no output schema and the description does not explain the normal return shape of a newly created index, only the duplicate-name case.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all six parameters in detail. The description still adds useful semantic guidance beyond the schema, especially the recommended default field configuration and the distinction between searchable fields and filter-only document metadata.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: create a Pinecone document index defined by searchable string fields. It explains the field modes and default configuration, but it does not explicitly differentiate this from sibling create-index-for-model, so an agent still has to infer that distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives implied workflow guidance by saying to use upsert-documents and search-document-index with the new index, and it explains that filter-only fields belong in metadata. However, it does not state when to choose this tool over alternatives such as create-index-for-model or list-indexes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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