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kanchana-mongodb
1e72df255e
* 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>
MongoDB (Self-Managed MCP)
For deployments you manage yourself — Community, Enterprise Advanced, a local dev container, or an Atlas cluster — this plugin connects your agent using nothing more than a connection string. It runs the MongoDB MCP server locally via npx, giving your agent tools to explore databases and collections, run queries and aggregations, and manage indexes.
To get started, see the Community & Enterprise Advanced setup guide.
Skills
These skills come bundled with the plugin and activate automatically when relevant:
mongodb-mcp-setup— Walks you through configuring the MCP server's connection string and environment variables.mongodb-connection— Reviews and tunes client connection configuration (pools, timeouts, patterns) across driver languages.mongodb-natural-language-querying— Turns natural-language requests into read-onlyfindqueries and aggregation pipelines.mongodb-query-optimizer— Diagnoses slow queries and recommends indexes to fix them.mongodb-schema-design— Applies MongoDB schema design patterns and steers you clear of common anti-patterns, whether modeling from scratch or migrating from SQL.mongodb-search-and-ai— Helps build Atlas Search (full-text), Vector Search (semantic), and hybrid search implementations.mongodb-atlas-stream-processing— Manages Atlas Stream Processing workspaces, connections, and processors for streaming workloads.