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Head to head · Analytics query · October 2026 research run

Countly vs Optimizely Experimentation

Optimizely Experimentation scores 62.6 (B) on agent readiness against Countly's 54.8 (C), and leads in 4 of 7 scored categories. Countly leads on payments & pricing. Both do analytics query.

CountlyCountly Ltd · HTTP API C54.8/100#607 of 841 Optimizely ExperimentationOptimizely · HTTP API B62.6/100#383 of 841

Which one, for what

Countly C

Good for An agent whose owner runs Countly on its own infrastructure or a dedicated Flex server and wants analytics, crash and remote-config data through MCP with per-category permissions.

Ahead on

  • Payments & pricing, 17 against 0

Also in its favour

  • Runs on your own machine
  • Open source

Watch for

No public status page or incident history was found for Flex servers or for mcp.count.ly

Optimizely Experimentation B

Good for An agent working for an existing Optimizely customer that lists and creates flags, experiments and audiences, reads experiment results and writes SDK integration code.

Ahead on

  • Reliability, 73 against 28
  • Transparency & trust, 75 against 70

Watch for

No price is published. The plans page says every plan is individually packaged and leads to a demo request

Score by category

CategoryWeight this runCountlyOptimizely ExperimentationEdge
Reliability16%202873Optimizely Experimentation +45
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27574Countly +1
Agent ergonomics13%16.26668Optimizely Experimentation +2
Security & auth14%17.56366Optimizely Experimentation +3
Payments & pricing10%12.5170Countly +17
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88078Countly +2
Transparency & trust7%8.87075Optimizely Experimentation +5
Negative events≤1500
Total54.8 · C62.6 · B

Facts side by side

FactCountlyOptimizely Experimentation
KindHTTP APIHTTP API
VendorCountly LtdOptimizely
Hosted endpointhttps://mcp.count.ly/mcphttps://exp.mcp.opal.optimizely.com/mcp
TransportsHTTP, stdio, Streamable HTTPHTTP, Streamable HTTP
AuthAPI keyOAuth or key
PricingPaidPaid
x402nono
LicenceCountly Lite is AGPL-3.0 with added branding terms under Section 7. Enterprise plugins are proprietary. Flex is a hosted service under Countly's terms. The MCP server is MITProprietary hosted service under Optimizely's Online Software Subscription Agreement and its API, SDK and MCP terms. The Feature Experimentation SDKs on GitHub are Apache 2.0
Tools exposed2097
Read-only variant documentedyesno
llms.txtyesyes
Last release2026-10-072026-09-18
Terms last updated2026-09-25no date given
Privacy policy last updated2026-09-25no date given
Customer content may train modelsnot found in the textnot found in the text
Terms restrict automated accessnot found in the textnot found in the text
Terms restrict benchmarkingnot found in the textyes
Terms or service can change without noticeyesnot found in the text
Arbitration or class-action waivernot found in the textnot found in the text
Popularity28k npm/wk460k npm/wk, 86k PyPI/wk

Verdicts

Countly

Countly suits an agent working against a server its owner already runs. The official MCP server filters its 209 tools by edition, plugin and permission and has per-category read-only settings. No status page, OpenAPI document or published API rate limit was found, and the Flex trial needs a credit card.

Optimizely Experimentation

Optimizely Experimentation suits agents working for an existing customer that manage flags and experiments and read results. The hosted MCP server has seven tools behind OAuth and carries the user's permissions, and the main REST API has a public Swagger spec. No price, free plan or trial is published, and no 429 or idempotency guidance was found.

Before you call either

Countly

  1. Create a dedicated token in the Token Manager, limited to the apps and endpoints the task needs, and send it in the countly-token header, not the URL
  2. Set COUNTLY_TOOLS_ALL=R unless the task needs writes. The token's own permissions still apply and the stricter of the two wins
  3. Call apps_list first. Most tools need app_id or an exact app_name
  4. Check get_version for the detected edition before planning funnel, retention or cohort queries, which need Enterprise plugins
  5. Set ENABLE_ANALYTICS=false to stop usage reports to stats.count.ly, and prefer the stdio server to mcp.count.ly so the server URL and token stay local

Optimizely Experimentation

  1. Connect to https://exp.mcp.opal.optimizely.com/mcp and complete OAuth in a browser. The session is time-limited, so expect to sign in again
  2. Call exp_get_schemas before exp_execute_query, and exp_get_entity_templates before exp_manage_entity_lifecycle, to learn the fields each entity needs
  3. Confirm the project and environment with the user before any create or update. Changes go to live Optimizely data
  4. Use the Visual Editor for Web Experimentation variation code. The MCP server does not write custom HTML, CSS or JavaScript
  5. Over REST, stay under 2 requests a second on /flags/v1, 100 a minute on /v2 and 20 a minute on the results endpoints

Questions

Which is better for AI agents, Countly or Optimizely Experimentation?

Optimizely Experimentation scores 62.6 (B) on agent readiness against Countly's 54.8 (C), and leads in 4 of 7 scored categories. Countly leads on payments & pricing.

Do Countly and Optimizely Experimentation need an API key?

Countly needs an API key. Optimizely Experimentation takes an API key or an OAuth sign-in.

Can an agent call Countly and Optimizely Experimentation without installing anything?

Yes. Countly has a hosted endpoint at https://mcp.count.ly/mcp and Optimizely Experimentation at https://exp.mcp.opal.optimizely.com/mcp.

Are Countly and Optimizely Experimentation open source?

Countly is open source (Countly Lite is AGPL-3.0 with added branding terms under Section 7. Enterprise plugins are proprietary. Flex is a hosted service under Countly's terms. The MCP server is MIT). No open-source release is listed for Optimizely Experimentation.

Other comparisons with Countly or Optimizely Experimentation

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