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pinecone-io

Pinecone Developer MCP

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by pinecone-io

Describe Index Stats

describe-index-stats
Read-only

Retrieves Pinecone index statistics, including record counts per namespace, to help you discover which namespaces exist and verify indexed data.

Instructions

Describe the statistics of a Pinecone index, including record counts per namespace. Use this to discover which namespaces exist in an index. Newly upserted records can take a few seconds to be indexed and become visible to search results and index stats; if recent data is missing, wait briefly and retry.

Input Schema

NameRequiredDescriptionDefault
nameYesThe index to describe.
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

A4/5.0
Behavior4/5

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

The readOnlyHint annotation already establishes that this is a safe read operation. The description adds valuable behavioral context beyond the annotation: newly upserted records may not appear immediately, and callers should retry after a brief wait. It does not cover authentication or rate-limit behavior, but those are less central for a read-only stats call.

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 three focused sentences with no wasted language. The core purpose is front-loaded, the namespace-discovery use case follows, and the eventual-consistency caveat is appropriately placed.

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?

For a read-only tool with full parameter schema coverage and no output schema, the description provides enough context to call it correctly. It explains the key return content at a high level and the indexing-delay caveat. It could be slightly richer about what statistics are returned, but the essential information is present.

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

Parameters3/5

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

Schema description coverage is 100%, so the parameters are already well documented in the schema. The description does not add meaning beyond the schema for name, llm_model, or llm_provider. With the schema doing the heavy lifting, this lands at the baseline for high-coverage parameter documentation.

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 gives a specific verb and resource ('Describe the statistics of a Pinecone index') and adds useful scope ('including record counts per namespace'), which helps distinguish it from generic index metadata tools. It does not explicitly name sibling tools such as describe-index, so sibling differentiation is implied rather than stated.

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

Usage Guidelines4/5

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

It clearly states a use case: 'Use this to discover which namespaces exist in an index.' It also gives practical guidance for eventual-consistency behavior ('wait briefly and retry'). It stops short of naming when to use alternatives such as describe-index or list-indexes.

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