What is marklogic-query-authoring?

A guide for writing MarkLogic queries, where MarkLogic is a database for searching documents, structured data, graphs, and vectors. It maps goals such as full-text search, filters, facets, joins, graph traversal, and similarity search to the appropriate query method.

Use it to write document searches, exact filters, value counts, facets, Optic queries for grouped or joined data, SPARQL graph queries, vector similarity searches, query-by-example searches, and geospatial searches.

It reduces empty or incorrect results caused by guessing field names, query types, or required indexes. It first checks the database schema, indexes, and collections so queries match the data that actually exists.

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-query-authoring/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-query-authoring/github.svg)](https://agentmods.dev/skills/tternquist/marklogic-mcp/marklogic-query-authoring)

Your own site

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

Per session 184 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,836 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	$0.00184	$0.02836
Opus 5.5	$0.00074	$0.01134
Sonnet 5.5	$0.00037	$0.00567
Haiku 4.5	$0.00018	$0.00284

Measured 1mo ago against content hash bb067c1bbc23, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

marklogic-query-authoring 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 1mo 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-query-authoring/SKILL.md · 205 lines

How it starts

The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MarkLogic Query Authoring

Pick the tool from the goal

Goal	Tool	Index prerequisite
Free-text across whole documents	`ml_search` with `q`	universal index (on by default)
Exact value on a JSON property	`ml_search` with `structured_query`	none — value index is on by default
Tokenised text in one field	`ml_search` `word-query`	none
Distinct values / counts / buckets	`ml_values_query`	range index on the field
Facet counts alongside results	`ml_facets_query`	range index per facet
Search UI: qtext + facet filters + sort	`ml_search` + options set (or raw combined query)	range index per constraint
GROUP BY, joins, aggregates	`ml_optic_query`	TDE template in Schemas
Entity relationships, graph traversal	`ml_sparql_query`	triple index on
Semantic / similarity search	`ml_vector_search`	vector index (ML 12+)
Query-by-example from a sample doc	`ml_search_qbe`	none
Geospatial	`ml_geospatial_search`	geospatial index

Before composing anything non-trivial, run ml_schema_discover (structure), ml_indexes_list (what is actually indexed), and ml_collections_list (what exists). Guessing at field names is the most common cause of empty results.

The most important rule: you usually don't need a range index

cts.parse (via ml_parse_query) requires a range index on every tagged binding — tags become cts.<kind>Reference objects. On a non-indexed field it fails with XDMP-ELEMRIDXNOTFOUND. That is a limitation of cts.parse, not of MarkLogic.

For exact-value filtering on a non-indexed JSON property, skip cts.parse and pass a structured query straight to ml_search:

{"query":{"value-query":{"json-property":"incidentType","text":["Hurricane"]}}}

For tokenised free text in one field:

{"query":{"word-query":{"json-property":"description","text":["hurricane"]}}}

For free text across the whole document, just use ml_search q="hurricane".

Read the full file on GitHub · 205 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

1mo ago Changed · +17 lines · +36 tokens per session bb067c1bbc23
2mo ago First seen · 188 lines · 148 tokens per session scan A 38f4f63b9d37

Subscribe to this mod's changes

marklogic-query-authoring is a skill published in the GitHub repository tternquist/marklogic-mcp (4 stars, last pushed 1mo ago), licensed MIT. It adds 184 tokens to every session and 2,836 once invoked, about $0.0007 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-query-authoring next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
marklogic-query-authoringtternquist/marklogic-mcp	Skill	4	1mo ago	MIT	A	184	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