mrkeyoor.com_
Tue 01 Sept 16:14 UTC
DatabasesMCP servermeasured 19 Aug 2026

Pinecone

Pinecone's own MCP server, published as @pinecone-database/mcp, gives an assistant stdio access to a Pinecone vector database: list and describe indexes, create an index backed by a hosted embedding model, upsert records, run semantic and cascading search, rerank results, and query Pinecone's documentation. It reported itself as pinecone-mcp version 0.3.0 when we started it.

BootBOOTS ✓We started it and initialize came back in 8.2s.
Tools9tools/list payload 24,664 bytes
Context tax~6,165estimated tokens for the whole tools/list, chars/4 estimate
Resources / prompts / counts returned by resources/list and prompts/list
Measured19 Aug 2026one run of the harness, clean environment, no credentials · how we test

How we started it

npx -y @pinecone-database/mcp
Verdict

Use it if Pinecone is already where your vectors live and you want an agent to build and query indexes without you writing glue. The honest cost is the context bill: 9 tools weigh in at 24664 bytes of tools/list, about 6165 tokens by the chars/4 estimate, and that gets re-sent on every turn of every conversation whether the agent touches a vector or not. Two search tools alone account for over a thousand estimated tokens each. If your agent queries an index occasionally rather than constantly, load it in a dedicated profile instead of your default one.

Use it if

You are building a retrieval pipeline on Pinecone and want the agent to create the index, upsert records and check describe-index-stats without leaving chat
You want cascading-search and rerank-documents available to an agent rather than reimplementing Pinecone's reranking in your own code
Your team keeps asking the assistant how a Pinecone feature works, and search-docs answering from the vendor's own documentation beats guessing
You are prototyping index configurations and the create-index-for-model tool saves you a trip to the console

Skip it if

Your vectors live in pgvector, Qdrant or Weaviate; nothing here is portable
You keep one always-on tool profile and cannot spare roughly 6165 estimated tokens for a database the agent touches once a week
You only ever read from a fixed index, in which case a single search function in your own code is cheaper than 9 tools
You are unwilling to hand a long-lived Pinecone API key to an assistant that can also create indexes and upsert records
You need namespace-level or metadata-heavy operations beyond what these 9 tools expose, and will end up using the SDK anyway

The bill for your context window

ToolEst. tokensDescribed?Required params
search-records~1,212yes (511 chars)3
cascading-search~1,185yes (265 chars)3
create-index-for-model~936yes (371 chars)2
rerank-documents~802yes (292 chars)3
upsert-records~621yes (401 chars)3
describe-index-stats~447yes (305 chars)1
describe-index~445yes (307 chars)1
list-indexes~387yes (167 chars)0

The tools/list payload came to 24664 bytes, an estimated 6165 tokens at 4 chars per token, measured 2026-08-19. That is a lot for 9 tools, and the weight is not evenly spread: search-records is the single heaviest at about 1212 estimated tokens, cascading-search sits just behind it near 1185, and create-index-for-model adds roughly 936. The schemas, not the descriptions, are doing this. search-records carries a 511 character description but four times that in estimated tokens, because the filter and query shapes are deep. At the light end, search-docs costs around 130 estimated tokens and needs one required parameter, which is a good deal if documentation lookup is all you want. If your agent only reads, the index-management half of this server is dead weight you pay for on every turn.

Setup reality

It runs under npx with no install step and no config file. We booted it with a clean environment and it came up anyway, printing a warning to stderr that PINECONE_API_KEY is not set and that the database tools will return an error until it is provided, then announcing it was running on stdio. So the boot you see in our record is a boot without credentials: everything except search-docs will fail until you put a real key in the environment. The harness recorded no other env hints. Transport is stdio, and it negotiated protocol version 2025-06-18.

Questions people ask

Does the Pinecone MCP server work without an API key?

It starts, but only search-docs is useful. When we ran it with a clean environment it warned on stderr that PINECONE_API_KEY was not set and that the database tools would error until it is provided. Set the key before you expect any index operation to succeed.

How much context does the Pinecone MCP server use?

Its tools/list payload measured 24664 bytes on 2026-08-19, an estimated 6165 tokens using the chars/4 method. That is charged on every conversation turn where the server is loaded, not per tool call.

Which Pinecone MCP tools are the expensive ones?

search-records at roughly 1212 estimated tokens and cascading-search at about 1185 lead, with create-index-for-model near 936. Together they carry most of the payload. Disabling the write and index-creation tools in a read-only profile is the easiest saving available.

Is this the official Pinecone server?

It is published by Pinecone under the @pinecone-database npm scope, and the identity it reports over the protocol is pinecone-mcp version 0.3.0. We have it flagged unofficial in our records pending confirmation, so treat the vendor scope as the stronger signal.

Other databases servers we started

ServerVendorBootsContext tax
ChromaChroma~0 tokens
ElasticsearchElastic~0 tokens
MongoDBMongoDB~9,462 tokens
MySQLcommunity (benborla29)~100 tokens
PostgreSQL (archived)Anthropic (archived)~33 tokens
QdrantQdrant~0 tokens
SupabaseSupabase~0 tokens

How this page is made: the server is spawned as an unprivileged user with a clean environment and no credentials, then asked for its tools, resources and prompts over stdio. Token figures are estimates at four characters per token, not a tokenizer count. One run, one machine. Corrections: contact the desk.

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