Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tternquist/marklogic-mcp/marklogic-query-authoringnpx skills add tternquist/marklogic-mcp --skill marklogic-query-authoringgit clone --depth 1 https://github.com/tternquist/marklogic-mcpWrote 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.
[](https://agentmods.dev/skills/tternquist/marklogic-mcp/marklogic-query-authoring)<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="Measured on agentmods" height="20"></a>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 | $0.00148 | $0.02532 |
| Opus 5 | $0.00074 | $0.01266 |
| Sonnet 5 | $0.00030 | $0.00506 |
| Haiku 4.5 | $0.00015 | $0.00253 |
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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 188 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 |
| 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".
Reach for ml_parse_query only when you specifically need string-grammar parsing — an
LLM-written X AND Y NOT Z expression with range comparisons on indexed fields — or
when round-tripping a string query through MarkLogic's parser to canonicalise it.
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.
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.
- 4d ago First seen · 188 lines · 148 tokens per session scan A 38f4f63b9d37
marklogic-query-authoring is a skill published in the GitHub repository tternquist/marklogic-mcp (3 stars, last pushed 14d ago), licensed MIT. It adds 148 tokens to every session and 2,532 once invoked, about $0.0007 per session on Opus 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.
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