What is marklogic-performance?

A guide for diagnosing slow or resource-heavy MarkLogic systems. MarkLogic is a database designed for document, search, graph, and analytics workloads.

Use it to investigate slow searches, Optic or SPARQL queries, timeouts, high memory use, and ingestion or merge problems, including how to read relevant diagnostics.

It helps identify whether delays come from indexes, document filtering, caches, queries, memory use, ingestion, or background merges.

How tternquist/marklogic-mcp configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to tternquist/marklogic-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/tternquist/marklogic-mcp/master/.claude/skills/marklogic-performance/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/tternquist/marklogic-mcp

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/tternquist/marklogic-mcp/marklogic-performance/github.svg)](https://agentmods.dev/skills/tternquist/marklogic-mcp/marklogic-performance)

Your own site

<a href="https://agentmods.dev/skills/tternquist/marklogic-mcp/marklogic-performance"><img src="https://agentmods.dev/badge/skills/tternquist/marklogic-mcp/marklogic-performance/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/tternquist/marklogic-mcp/marklogic-performance"><img src="https://agentmods.dev/badge/skills/tternquist/marklogic-mcp/marklogic-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,728 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00121	$0.01728
Opus 5.5	$0.00048	$0.00691
Sonnet 5.5	$0.00024	$0.00346
Haiku 4.5	$0.00012	$0.00173

Measured 2mo ago against content hash ac104a0fb011, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

marklogic-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/marklogic-performance/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MarkLogic Performance

Architecture in one paragraph

E-nodes (Evaluator) parse requests, execute XQuery/SJS, and do filtering, snippeting, and all SPARQL joins. D-nodes (Data Manager) store data and indexes, resolve indexes, read from disk, and run background merges. A combined node does both. Around 16+ nodes, separate E and D for analytics workloads.

The two-step search process — the usual culprit

Index resolution (D-nodes) → candidate fragment IDs from indexes.
Filtering (E-nodes) → load each candidate document and verify the full match.

cts:search runs filtered by default. When a query is fully backed by range and word indexes, add the "unfiltered" option and step 2 disappears.

Diagnostics:

ml_profile_query → filterMisses > 0 means step 2 is doing real work.
cts:contains(result, query) returns false for a false positive — a quick way to measure the false-positive rate directly.

Reading cache stats

Cache	Holds	A miss means
List cache	index term lists (D-node)	disk read during index resolution
Compressed tree cache	document bodies (D-node)	disk read during filtering
Expanded tree cache	uncompressed doc trees (E-node)	document expansion work
Triple cache	triple data for SPARQL	normal on first query

Always compare a cold run against a warm run. Many misses on the first query are expected; the same misses on repeat runs indicate a structural bottleneck, not startup.

When a range index is mandatory

cts:range-query, cts:element-range-query
ORDER BY in a FLWOR — on the ORDER BY field (last XPath step)
ml_values_query, ml_facets_query — range index or element word index
Optic ORDER BY — on the sort column; without one, all documents load to sort

Missing range index + filtered search is the worst case: a full document scan. Run ml_indexes_list before writing any range-dependent query.

Optic rules

ml_explain_optic shows the plan. Read the node types:

Read the full file on GitHub · 137 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 137 lines · 121 tokens per session scan A ac104a0fb011

Subscribe to this mod's changes

marklogic-performance is a skill published in the GitHub repository tternquist/marklogic-mcp (4 stars, last pushed 1mo ago), licensed MIT. It adds 121 tokens to every session and 1,728 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Databases

marklogic-performance next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
marklogic-performancetternquist/marklogic-mcp	Skill	4	1mo ago	MIT	A	121	Claude Code
create-prprisma/orm	Skill	48k	yesterday	Apache-2.0	A	48	Claude Code, Codex
schema-explorationlangchain-ai/deepagents	Skill	31k	yesterday	MIT	A	57	Claude Code, Codex
atlas-mssqlariga/atlas	Skill	8.8k	yesterday	Apache-2.0	A	163	Claude Code
ha-data-storesshiwenwen/hope-agent	Skill	1.9k	yesterday	MIT	A	115	Claude Code, Codex
nornicdb-cypher-queriesorneryd/NornicDB	Skill	923	yesterday	MIT	A	79	Claude Code, Codex
volcengine-rds-postgresqlbytedance/agentkit-samples	Skill	484	today	Apache-2.0	A	63	Claude Code, Codex

marklogic-mcp: Skill for Claude Code