GTM MCP Bench

This benchmark measures how effectively agents are able to prospect, create campaigns, and manipulate data using the MCPs available for common GTM tools.

Why we built this. We wanted to understand where agents get stuck when using MCPs to complete real outbound workflows. These results are a first step, and we plan to build on this benchmark by adding more harnesses & models, and more tasks covering a broader range of use cases. We use the results to improve Cluster’s MCP.

How it works. We compare the performance of the MCPs across 26 tasks spanning common outbound workflows like campaign creation, prospecting, and list building.

  • Campaign creation: 8 tasks
  • Prompt to campaign: 9 tasks
  • Qualification: 7 tasks
  • CSV import: 2 tasks

The tasks use anonymized queries derived from real user examples.

For each (task, provider) combination we start a fresh Codex CLI agent (Using GPT 5.5 xhigh) with a task brief, any supplied inputs, and the provider's MCP. The agent discovers tools and works through the task. We record its time and token usage, then verify the agent produced the expected outputs to pass the task (e.g. creating a campaign).

We use the publicly hosted MCP server for each provider. Tests were last run on: .

Task coverage

Supported tasks in the tested MCP configuration, separate from trial completion.
MCP providerCampaign creation8 tasksPrompt → campaign9 tasksQualification7 tasksCSV import2 tasksTotal coverage26 tasks
Cluster8 / 89 / 97 / 72 / 226 / 26
Lemlist7 / 89 / 97 / 72 / 225 / 26
Clay0 / 80 / 97 / 70 / 27 / 26
HeyReach4 / 80 / 90 / 71 / 25 / 26

Coverage means supported by the MCP configuration tested, not a successful run or a claim about the entire product. Clay’s connected MCP lacked campaign creation; HeyReach’s lacked email and native research.

Campaign creation

The agent receives recipients and message copy, then saves a draft with the required sequence, variables, and delays. Tasks range from 3 to 1,000 contacts. Seven tasks are shared by Cluster and Lemlist; the average compares those tasks.

Campaign creationToken cost. Costs and times include all measured runs; completion is reported separately.
TaskClusterLemlistClayHeyReach
Basic email — 3 leads$0.48$1.04Not supportedNot supported
Basic LinkedIn — 3 leads$0.54$1.03Not supported$1.03
Personalized LinkedIn — 40 leads$0.69$1.42Not supported$1.09
LinkedIn + email — 40 leads$0.64$1.53Not supportedNot supported
Connection requests, CSV — 87 leads$0.54$0.88Not supported$3.46
Connection requests, Markdown — 87 leads$0.64$1.22Not supported$4.28
Email — 1,000 leads$0.54$0.97Not supportedNot supported
LinkedIn + image — 3 leads$0.45Not supportedNot supportedNot supported
Average$0.587 tasks$1.167 tasksNot supported$2.464 tasks

The average uses seven tasks for Cluster and Lemlist, and four supported tasks for HeyReach. All measured runs are included. Costs cover the agent’s model tokens, excluding product credits and subscriptions.

Prompt to campaign

Given a targeting brief, the agent finds people, researches them, and saves a personalized draft campaign. Nine tasks are shared by Cluster and Lemlist.

Prompt to campaignToken cost. Costs and times include all measured runs; completion is reported separately.
TaskClusterLemlistClayHeyReach
Restaurant-group alumni$1.38$7.21Not supportedNot supported
Experienced benefits brokers$2.52$4.33Not supportedNot supported
Boston ecommerce operators$2.27$10.21Not supportedNot supported
AI-native GTM teams$2.51≥ $12.87Not supportedNot supported
Indian developer decision-makers$1.59$6.14Not supportedNot supported
Retail/media directors and VPs$1.44$6.32Not supportedNot supported
Enterprise CIOs$1.57$9.21Not supportedNot supported
JLL & CBRE leasing brokers$1.95$7.82Not supportedNot supported
CPO / COO targeting — 40 leads$1.43$2.33Not supportedNot supported
Average$1.859 tasks≥ $7.389 tasksNot supportedNot supported

One selected trial per provider per task; all measured runs are included. ≥ marks a lower bound because one Lemlist run timed out with partial token accounting. Completion checks the saved campaign structure.

Example task: Northeastern alumni in restaurant groups

One selected trial per provider. These figures describe this task only.

Task brief · condensed

Find 10 Northeastern University alumni currently working at US-headquartered restaurant companies.

Create a personalized draft LinkedIn campaign. Save a distinct, factual opening line and a supporting public URL for each person. Do not send.

Agent run

Cluster

21 turns

$1.38 in tokens

4m 36s

Lemlist

49 turns

$7.21 in tokens

9m 42s

Each block is one agent turn.

Output summary · both providers

LinkedIn campaign

10 prospectsDraft saved
  1. Connection request

    Wait 1 day

  2. Personalized message

    Wait 3 days

  3. Follow-up message

Personalized opening lines and supporting URLs saved per contact.

Both drafts passed structural checks. Research accuracy was not independently audited. Token costs exclude product credits.

Qualification

Given a list and qualification criteria, the agent uses each platform’s native research tools to return a decision and explanation for each entry. Results cover six shared tasks across Cluster, Lemlist, and Clay.

QualificationToken cost. Costs and times include all measured runs; completion is reported separately.
TaskClusterLemlistClayHeyReach
Air-export fit$0.64$7.25$4.56Not supported
Conditional qualification tiers$0.77$1.55$1.10Not supported
Current technical role$0.66$2.30$2.42Not supported
Production agents & HQ$0.60$4.87$1.81Not supported
R&D with exceptions$0.72$2.28$2.85Not supported
Security operators$0.73$5.61$2.16Not supported
Average$0.696 tasks$3.986 tasks$2.486 tasksNot supported

Tasks are weighted equally, with all measured runs included. The 100-person task was excluded after a callback failure.

Example task: Production agents and headquarters
Cluster

9MCP calls

Configure a research column, run the batch, wait for the results.

Lemlist

22MCP calls

Set up a campaign and AI variable, add leads, then check and troubleshoot.

Clay

41MCP calls

Separate company-research requests and result checks for each company.

This describes the company-research tools used in this run; Clay also has batch contact enrichment. A Lemlist fallback hit the harness’s approval policy.

CSV import

The agent imports a small list or appends two CSVs into one destination. The two tasks are each run five times per supported provider and checked for preservation of the supplied rows and fields.

CSV importToken cost. Costs and times include all measured runs; completion is reported separately.
TaskClusterLemlistClayHeyReach
Small CSV — 6 rows$0.52$0.86Not supported$0.91
Two CSVs — 135 rows$0.44$0.74Not supportedNot supported
Average$0.482 tasks$0.802 tasksNot supported$0.911 task

Each task averages five trials, including failures. The average row weights supported tasks equally. Completion is the share of trials that fully passed the row and field preservation checks.

Methodology

Run by Cluster to evaluate our MCP alongside other providers. Results reflect selected runs.

Each trial starts a fresh Codex CLI agent (Using GPT 5.5 xhigh) with a task brief, any supplied inputs, and one provider’s publicly hosted MCP server. The agent discovers the available tools and decides how to work through the task.

We record agent time, model-token usage, and tool calls, including recovery from errors. After the agent finishes, we check the saved product state and recorded evidence against the task’s requirements to determine whether it passed.

What does token cost include?
The dollar figures measure the outer Codex agent’s model-token usage, including recorded cached input usage. They exclude platform subscriptions, enrichment credits, and research costs inside each product. A cheaper agent run does not necessarily mean a cheaper total workflow. These are costs per task, not per lead.
What counts as completion?
Campaign checks cover the saved draft, recipients, sequence, delays, variables, and required personalization. Qualification checks cover matched inputs, decisions, and explanations. This is structural completion; it does not independently establish research accuracy. Campaigns were not launched, so delivery, replies, and meetings booked are outside the benchmark.
Are all providers measured under identical conditions?
Paired tasks use the same brief and inputs, with each provider’s publicly hosted MCP server. Native qualification uses the platform’s default research model.