* feat(mongodb-search-and-ai): add Automated Embedding, refine skill after review
Add Automated Embedding (autoEmbed) support and polish the skill following a
full /review-skill pass (structural validation + LLM scoring + live cluster
validation against sample_mflix.movies).
Changes:
- SKILL.md: add "Automated Embedding" search type, cluster-tier check step,
Core Principle #6 (accessible language), condense $regex/$text anti-pattern,
trim redundant "Remember" section; restore license/metadata frontmatter to
match sibling skills.
- references/automated-embedding.md: new reference for autoEmbed index types
and text-query (query vs queryVector) syntax.
- references/vector-search.md: consolidate pre/post-filter docs, fix dead TOC
anchor (#filter-fields), remove duplicated performance notes and score caveats.
- references/hybrid-search.md: $rankFusion (8.0+) / $scoreFusion (8.2+) updates.
Review outcome: structural validation passed; SKILL.md overall 3.83 -> 4.0,
reference aggregate 4.4 -> 4.6, no dimension below 3. Live-validated the
highest-novelty autoEmbed query/queryVector distinction against a live Atlas
cluster.
* test(mongodb-search-and-ai): add Automated Embedding evals + iteration-1 results
Add three Automated Embedding eval cases (ids 16-18) covering the autoEmbed
index type, plain-text query field (vs queryVector), and model selection
(voyage-4-lite for cost, voyage-code-3 for code). Add SUMMARY.md recording the
iteration-1 skill-creator run: with_skill 95.2% vs without_skill 43.4%
(+51.8%), Claude Opus 4.6, MongoDB MCP against sample_mflix. All 18 evals
differentiate; the new autoEmbed cases show +75%/+100%/+75% deltas.
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* chore(mongodb-search-and-ai): sync plugin skill copies
Regenerate the mongodb and mongodb-atlas plugin copies of the
mongodb-search-and-ai skill from the canonical skills/ source so the
plugin mirrors reflect the Automated Embedding + review refinements.
Ran tools/sync-plugin-skills.ts; output is idempotent (no further drift).
Fixes the failing sync-check CI job on PR #57.
* fix(mongodb-search-and-ai): use valid mongosh syntax in autoEmbed index example
Address Copilot review on PR #57: the createSearchIndex example used
db.<collection>, which isn't valid mongosh syntax. Use db.collection to
match the convention in the sibling reference files (placeholders stay
inside strings).
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* AGSK-18 fixes version
* fix(mongodb-search-and-ai): correct version gates to 8.3+
SME correction: $scoreFusion and self-managed Automated Embedding require
MongoDB 8.3+ (previously documented as 8.2+). Normalize the $scoreFusion
note in SKILL.md to use the "+" suffix for consistency.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* fix(mongodb-search-and-ai): address Copilot review feedback
- Add "Automated Embedding" to the workflow proceed-gate in SKILL.md so
Automated Embedding requests don't dead-end after the Cluster Check.
- Make self-managed prerequisites edition-agnostic: drop "Community
Edition"/"Enterprise Edition" qualifiers in automated-embedding.md and
SKILL.md; key the requirement on MongoDB 8.3+ with mongot + Voyage AI.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* fix(mongodb-search-and-ai): correct rate-limit unit in autoEmbed upgrade note
Address Copilot review on PR #57: the free->paid upgrade note said
"667x higher TPM", but 667x is the RPM increase (3 -> 2,000 RPM). Reword
to state the RPM change explicitly and note TPM rises correspondingly, so
it matches the rate-limit tables above.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* docs(mongodb-search-and-ai): note Atlas prerequisites can change
Address Copilot review on PR #57: the Automated Embedding cluster tier
and autoscaling prerequisites were stated as absolutes. Add a brief note
in SKILL.md and automated-embedding.md to confirm current requirements in
the Atlas UI or official docs, since these can drift across releases.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* docs(mongodb-search-and-ai): cover item-to-item similarity for autoEmbed
Address jeff-allen-mongo's review on PR #57: the decision framework and
Automated Embedding query guidance didn't handle "given item A, find
similar items" requests. Since autoEmbed $vectorSearch.query only accepts
a plain text string (no raw-vector read path to reuse a document's stored
embedding), add:
- A clarifying Discovery question in SKILL.md (free text vs. similarity to
an existing item).
- An "Item-to-Item Similarity" callout in automated-embedding.md showing
how to resolve the source item to its indexed text field and pass that
as the query (with a self-exclusion filter), plus a TOC entry.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* docs(mongodb-search-and-ai): simplify Automated Embedding autoscaling note
Condense the M10+ dedicated cluster prerequisite to "enable storage
autoscaling" and align the Item-to-Item Similarity TOC entry as a
top-level bullet.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* docs(mongodb-search-and-ai): simplify autoEmbed auto-scaling prerequisite
Condense the Atlas Clusters prerequisite to "M10+ dedicated clusters
require storage auto-scaling to be enabled", dropping the detailed
max-tier table and explanatory paragraph. Fix subject/verb agreement in
the staleness note.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* docs(mongodb-search-and-ai): address Copilot review nits
- SKILL.md: standardize on "auto-scaling" (hyphenated) to match
references/automated-embedding.md and the Atlas UI spelling.
- references/vector-search.md: remove trailing whitespace on the
pre-filtering Definition line.
- references/hybrid-search.md: format all referenced filenames in the
Scope paragraph as inline code for consistency.
Regenerated plugin copies via tools/sync-plugin-skills.ts (idempotent).
* test(mongodb-search-and-ai): expect cluster check in autoEmbed evals
Align Automated Embedding eval cases 16-18 with the updated skill
workflow: each expected_output now requires a cluster check before
index creation - confirm Atlas (all tiers incl. M0) vs self-managed
(8.3+ with mongot + Voyage AI key), and call out the M10+ dedicated
storage auto-scaling prerequisite.
* chore(mongodb-search-and-ai): sync plugin mirrors for auto-scaling wording
Propagate the canonical SKILL.md edit ("M10+ without storage
auto-scaling") to the four plugin copies via tools/sync-plugin-skills.ts.
Fixes the failing Plugin Skills Sync check caused by mirror drift.
* fix(mongodb-search-and-ai): apply review suggestions and sync plugin mirrors
Co-authored-by: kanchana-mongodb <54281287+kanchana-mongodb@users.noreply.github.com>
* docs(mongodb-search-and-ai): address review feedback
Route search-type selection from step 2 and defer prerequisites to
reference files, per PR review.
- SKILL.md: merge search-type routing into "Determine Search Type"
(step 2); remove standalone Cluster Check / Version Check / Consult
Reference Files steps. Each search type links directly to its
reference file and defers prerequisite verification to that file.
- automated-embedding.md: move cluster/self-managed prerequisites and
the M10+ auto-scaling + Voyage AI fallback behavior into Prerequisites;
drop "Atlas UI" wording; fix inaccurate "no vector pipelines required"
intro; combine intro+scope; remove decorative "---" rules; drop the
duplicated "When to Use" routing block; document optional filter
type/path fields.
- vector-search.md / hybrid-search.md: combine intro + scope to reduce
redundancy; move hybrid version-gate fallback behavior into
hybrid-search.md.
- evals.json: fix missing comma (invalid JSON).
- Regenerate plugin skill mirrors via tools/sync-plugin-skills.ts.
* docs(mongodb-search-and-ai): fold in remaining Copilot suggestions
Address open Copilot review comments on the Automated Embedding
reference, then regenerate the plugin mirrors.
- automated-embedding.md: use a concrete `db.movies` collection name
in the createSearchIndex and item-to-item examples (instead of
`db.collection` / `db.<collection>`) for consistency with sibling
reference files; use the modern `{ projection: { plot: 1 } }`
findOne options form; add a note that `_id` is always available as a
filter field on an autoEmbed index (no need to declare it) for the
self-exclusion filter.
- Regenerate plugin skill mirrors via tools/sync-plugin-skills.ts.
* docs(mongodb-search-and-ai): clarify filter-field guidance
Address Copilot re-review comments on automated-embedding.md.
- Reword the self-referential filter-field inline comment to explain
the purpose (index fields as filters to enable pre-filtering /
scoped search) instead of "recommended for filter fields".
- Clarify the `_id` note so it no longer contradicts the Query
Parameters table: filter fields must generally be indexed as
type "filter", with `_id` called out as the one implicit exception;
add the same caveat to the table's `filter` row.
- Regenerate plugin skill mirrors via tools/sync-plugin-skills.ts.
* Update MCP config description in SUMMARY.md
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
kanchana-mongodb