Benchmarking Coding Agent Data Retrieval: Claude Code is 31% More Accurate and 3.4x More Cost Efficient with Coral

Coral enables AI agents and applications to query data across any API, database or file system with SQL. Today, AI agents commonly use data source provider MCP servers. We benchmarked Coral with direct provider MCPs (Datadog, Sentry, Linear, Slack and GitHub) for a diverse set of real-world AI tasks using Claude Opus 4.6.

Key findings

• Widespread impact on performance. Across all tasks, Claude was 20% more accurate and 2x more cost efficient using Coral than using direct provider MCPs. With Coral, Claude also had 42% lower latency.

• Highest impact on coding agent tasks. Across the more complex tasks that typify coding agent workloads (multi-hop, higher post-processing), Claude was 31% more accurate and 3.4x more cost efficient with Coral.

• More neutral impact on simpler tasks. For simpler AI tasks, such as raw fact retrieval from knowledge bases, the results were closer, with Claude 6% more accurate and 2% more cost efficient with Coral.

Methodology

The benchmark defined 82 real-world agent tasks ranging in processing and source complexity from single fact lookups, to conclusions requiring reasoning or aggregation across data from multiple sources. Each task was framed as one or more questions and was combined with factual assertions (243 in total) that a strong response should include.

We used real operational data from a B2B SaaS product using Linear, Datadog, Sentry, Slack and GitHub data sources. This data is a closer match to real-world agent scenarios than current public MCP benchmark datasets. The agent, Claude Code using Opus 4.6, ran in an isolated sandbox with no prior context. Claude Code uses a tool search capability to avoid loading all MCP tool definitions into the context window upfront: an LLM without tool search would have even larger token and cost efficiency gains from Coral.

The table groups the results into simple and high complexity. Task-level results are available in the appendix, along with their group categorisation.

Results by Task Type

Task type	Method	Fact Accuracy	Median Seconds	Median Tokens	Median Cost
Simple tasks (n = 31)	MCP	87 / 92	35	88,178	$0.14
	Coral	92 / 92	35	107,634	$0.14
	Coral Var	+6%	+1%	+22%	-2%
Complex tasks* (n = 51)	MCP	110 / 151	99	313,727	$0.54
	Coral	144 / 151	45	112,681	$0.16
	Coral Var	+31%	-55%	-64%	-70%
All task types (n = 82)	MCP	197 / 243	72	188,830	$0.33
	Coral	236 / 243	42	110,724	$0.16
	Coral Var	+20%	-42%	-41%	-52%

*Complex tasks require conditions, aggregation or post-processing

See all 82 task-level results →

Causes of Cost and Token Efficiency

SQL enables fewer, more precise queries. Coral has a structural advantage when agent query complexity exceeds what a single API call can answer. The SQL interface enables server-side aggregation instead of MCP’s O(N) pagination. When an agent uses MCP to paginate through large result sets to count or aggregate, the cost and latency compound. Token efficiency also benefits from cleaner, tabular query responses.

For example, a relatively simple task “What label groups do we use to categorize issues?” shows the efficiency gap even when both runners answer correctly. MCP brute-forces discovery, guessing filter names, querying labels by team, searching documentation, and listing issues to reverse-engineer the label structure, taking 29 tool calls over 134 seconds. Coral runs SELECT * FROM linear.issue_labels WHERE is_group = true, taking 6 calls over 21 seconds. The underlying data is the same but SQL makes it accessible in one step.

Cross-source queries. When the task requires correlation - matching entities across systems, comparing structures, aggregating - agent orchestration across MCP tool responses is token inefficient and sometimes leads to time-out failures and flooded context windows. Coral enables agents to query with cross-provider JOINs instead.

On simple single source tasks, when questions are narrow enough that a single API call resolves them without pagination, data provider MCP’s can be more token efficient than Coral, because Coral does schema discovery calls for unfamiliar data sources. Identity-detail tasks, for example questions like “describe this service” or “what does this dashboard monitor”, suit the approach of returning full-object API responses.

Causes of Accuracy Improvement

Fewer timeout failures. With provider MCPs, sometimes the LLM spends so many turns trying to find the right tool, or work around a missing tool, that it runs out of time and the output is either truncated, malformed, or missing entirely. This caused half of Coral’s accuracy advantage, enabling Claude to find 20 target facts that failed to be retrieved with MCPs.

For example, for the task “Do we have any duplicate dashboard names in our monitoring tool? If so, who created them?” the LLM called search_datadog_dashboards 56 times across 230 seconds. That Datadog tool doesn’t support aggregation, so the LLM tried to enumerate duplicates by searching for each title individually, then each dashboard ID, then each author handle. Eventually it failed. In contrast, the same task with Coral was solved accurately with 7 tool calls.

MCP data gaps. MCP servers often don’t include all of the data available from the API. For example Datadog’s MCP has 19 tools for dashboards, monitors, hosts, logs, and metrics - but no tool for user management. You can’t list users, check disabled status, or see account creation dates. In contrast, Coral connectors expose the full data model as SQL tables. This caused the remaining half of Coral’s accuracy advantage over MCP.