Published 2026-09-21 · Tested 2026-08-19

Zilliz Cloud

D-

Zilliz Cloud received 3 PASS votes and passed 3 of five agent surface checks. The clearest finding came from the find the exact limits task.

Panel: GPT 5.6 Sol, Opus 5, DeepSeek v4F Battery: v1 Read as markdown (opens in a new tab)

Three AI models, GPT 5.6 Sol, Claude Opus 5, and DeepSeek v4 Flash, each read Zilliz Cloud’s public documentation independently and attempted five first-hour developer jobs: run the first vector search, find the exact limits, recover from REST errors, verify an alert webhook, use PyMilvus.

No accounts, API calls, or code execution were used. Every verdict came from public pages and every published quotation passed a live verification check. Preflight substitutions: T3: Documented REST error recovery replaced HTTP 429 because it is the clearest common error path.

Freshness

How rechecks work
Tested
Quotes verified
Surface rechecked

No change since the test.

4 of 5 quoted passages still appear on the live pages. The quoted page changed for Use PyMilvus.

Zilliz Cloud Docusaurus 3.10.1 · published
D-

60.0% · 48/80 · AI Agent Readiness Score · reading 30 pts · surface 50 pts

llms.txt PASS
llms-full.txt FAIL
markdown mirror PASS
MCP server FAIL
docs AI PASS
Task GPT 5.6 SolOpus 5DeepSeek v4F Consensus
Run the first vector search PARTIALPARTIALPARTIAL PARTIAL
Find the exact limits PARTIALPARTIALPARTIAL PARTIAL
Recover from REST errors PASSPARTIALPARTIAL PARTIAL
Verify an alert webhook PASSPARTIALPARTIAL PARTIAL
Use PyMilvus PARTIALPARTIALPASS PARTIAL

docs platform: Docusaurus 3.10.1 (unscored) · verified 2026-08-19

What to fix first

These 7 fixes could add up to 32 points to the AI Agent Readiness Score. The list ranks each fix by the points it would add. How the ranking works

  1. 1
    +10 points llms-full.txt check failed

    Found: HTTP 404 at the tested documentation roots.

    Fix: Publish llms-full.txt at the docs root with the full text of every docs page in one plain-text file.

    Evidence: docs.zilliz.com/llms-full.txt (opens in a new tab)

  2. 2
    +10 points MCP server check failed

    Found: Initialize returned HTTP 404 at all tested candidate endpoints.

    Fix: Run a public MCP server for the docs that answers a JSON-RPC initialize call and offers a docs search tool.

    Evidence: docs.zilliz.com/mcp (opens in a new tab)

  3. 3
    +3 points Run the first vector search PARTIAL

    Found: The quickstart skips cluster creation and imports data from an external bucket instead of showing a vector insert.

    Fix: Add a quickstart path that creates a cluster, inserts vectors with insert(), and runs one search.

    Evidence: docs.zilliz.com/docs/quick-start (opens in a new tab)

  4. 4
    +3 points Find the exact limits PARTIAL

    Found: The limits page gives 20 load requests per second per cluster and no DDL rate limit for Free clusters. The collection FAQ gives 1 load request per second.

    Fix: Give one load-request limit and scope on the limits page and collection FAQ, plus the DDL limits for Free clusters.

    Evidence: docs.zilliz.com/docs/limits (opens in a new tab)

  5. 5
    +2 points Recover from REST errors PARTIAL

    Found: The 96016 error entry says to check the API key scope but gives no link or steps to change it.

    Fix: Link error 96016 to the API key management guide, and list the role each REST operation requires.

    Evidence: docs.zilliz.com/reference/restful/error-codes-v2 (opens in a new tab)

  6. 6
    +2 points Verify an alert webhook PARTIAL

    Found: The notification channels page shows no test-message payload or passing test result. In the panel's reading, it also omits the HTTP method, content type, response code, and retries.

    Fix: Document the webhook HTTP method, content type, expected response code, retries, test-message payload, and what a passing test shows.

    Evidence: docs.zilliz.com/docs/manage-notification-channels (opens in a new tab)

  7. 7
    +2 points Use PyMilvus PARTIAL

    Found: In the panel's reading, the MilvusClient reference example uses a uri host that matches no documented endpoint pattern.

    Fix: Set uri in every PyMilvus example to an endpoint pattern from the connection endpoints page.

    Evidence: docs.zilliz.com/reference/python/python/Client-MilvusClient (opens in a new tab)

What the docs get right

  • Recover from REST errors: 1 PASS votes. The error table gives both corrections, and API Keys explains scope changes; /docs/authentication did not load.
  • Verify an alert webhook: 1 PASS votes. The guide covers webhook setup, shows a JSON notification body, and uses Send Test Message to verify configuration.
  • 3 of 5 agent surface checks. Present: llms.txt, markdown mirrors, docs AI. Missing: llms-full.txt, an MCP server.
PARTIAL

PARTIAL consensus from 3 PARTIAL.

The quickstart covers connection through search but assumes prepared external data instead of showing a direct vector insert. Cluster creation is fully documented on /docs/create-cluster with both console and REST paths, but the page titled Quickstart to Serving Cluster begins after cluster creation, never shows insert() at all (its only data step is a bulk import from an S3 bucket with access keys), and hardcodes the Dedicated-only endpoint form that /docs/access-connection-endpoints says Free and Serverless clusters must not use. The Quickstart, the most obvious path for a first vector search, skips cluster creation and imports data from external S3 storage rather than inserting individual vectors, forcing an agent to triangulate across three pages with no unified from-zero walkthrough.

Find the exact limits

PARTIAL

PARTIAL consensus from 3 PARTIAL.

The docs give collection, partition, and insert limits but omit Free DDL and conflict on load rates; /docs/limits-and-restrictions did not load. One canonical page gives collection limits per tier (Free 5, Serverless 100, Dedicated 1,024 per CU capped at 16,384), a tier-independent per-collection partition cap of 1,024, and insert rates per tier (Free 2 MB/s, Serverless 10 MB/s, Dedicated 16 MB/s + 1 MB/s per CU), with no contradictions against the FAQ or free-trial pages, but the 20 req/s DDL rate limit is scoped to Serverless and Dedicated only and the cluster-wide partition ceiling is a per-CU formula that cannot be evaluated for Free or Serverless because the same page states that the concept of CU only applies to Dedicated clusters. Cloud limits page covers all plans but the BYOC limits page omits Free and Serverless tiers entirely (no collection caps, no vCU limits, no capacity limits, no nq=10 cap), creating a contradiction that forces an agent to determine which page applies.

Recover from REST errors

PARTIAL

PARTIAL consensus from 1 PASS, 2 PARTIAL.

The error table gives both corrections, and API Keys explains scope changes; /docs/authentication did not load. Recovery from 96008 is exact and self-contained and matches the List Clusters (V2) spec default of 10 with minimum 1 and maximum 100, but 96016 only says to check the API key scope and no page maps a REST operation to the role it requires (the Authorization header is documented merely as an API key with appropriate privileges), so the fix requires jumping unlinked to /docs/manage-api-keys in Cloud Guides; separately, 185 of the 187 page URLs published inside https://docs.zilliz.com/llms/restful.txt carry a doubled path segment and return 404. Error codes are listed with their messages but recovery for 96016 stops at 'check your Apikey scope' with no link to API-key management docs or guidance on how to set the correct scope, requiring guesswork.

Verify an alert webhook

PARTIAL

PARTIAL consensus from 1 PASS, 2 PARTIAL.

The guide covers webhook setup, shows a JSON notification body, and uses Send Test Message to verify configuration. Console configuration steps and an example alert payload with orgId, projectId, summary, level, and timestamp are documented, and the Create Alert Rule (V2) REST spec exposes a WEBHOOK action type, but the docs never state the HTTP method, content type, expected response code, retry behaviour, or the shape of the test-message payload, the only verification step is clicking an icon identified solely by a screenshot, and the notification-channel guide never mentions the REST alert-rule API whose webhook field is named url while every other channel uses webhookUrl. Webhook setup and test message are documented but the test-message payload format is not shown, no expected success/failure feedback is described, and the example payload is a real alert rather than a connectivity-test payload.

Use PyMilvus

PARTIAL

PARTIAL consensus from 1 PASS, 2 PARTIAL.

The flow is complete, but PyMilvus guidance conflicts between 2.6.9 and 2.6.12; /reference/python/MilvusClient did not load. Installation, the token format of an API key or colon-joined username and password, and create_collection plus list_collections are all documented and mutually consistent, but every runnable Python example sets uri to a host that matches none of the three endpoint patterns printed on the same page, and the list_collections page opens by saying the method applies to dedicated serving clusters and on-demand compute while immediately listing Free and Serverless endpoints for it. PyMilvus installation, MilvusClient initialization with cluster credentials, and collection creation are documented with consistent, copyable code examples across two pages with no contradictions.

The receipt

Currently, the rate limit for loading collection requests on Zilliz Cloud is 1 per second.

The docs give collection, partition, and insert limits but omit Free DDL and conflict on load rates; /docs/limits-and-restrictions did not load.

Agent surface notes

Initialize returned HTTP 404 at all tested candidate endpoints.

The live docs expose a public Ask AI control.

Show the score

AI Agent Readiness Score 60.0%, grade D-

Paste this into a readme:

[![AI Agent Readiness Score 60.0%](https://docsforagents.com/badge/zilliz.svg)](https://docsforagents.com/reports/zilliz-docs-ai-agent-readiness/)

Method note

This is a reading test of public documentation, not an execution test. No accounts were created and no API calls were run. The AI Agent Readiness Score counts fifteen reading votes at PASS 2, PARTIAL 1, and FAIL 0, for 30 possible points. Five agent surface checks add 10 points each. The total is 80. Consensus chips show each row majority and do not affect scoring. The panel split on 3 of five tasks. Quotes shown here were re-fetched and confirmed verbatim on 2026-08-19.

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