"PostgreSQL" matching MCP tools:

• sixta_explain_planA

  sixta-connectA

  Turn raw EXPLAIN output into a plain-language diagnosis — no query needed. Paste PostgreSQL EXPLAIN / EXPLAIN ANALYZE (text or JSON) or MySQL EXPLAIN (tabular, \G, FORMAT=JSON, FORMAT=TREE) and get: what the planner is doing step by step, where the cost concentrates, named risk findings (full scans, spilling sorts, nested-loop blowups, row misestimates) with index suggestions, and what to look at next. Use when the user pastes EXPLAIN output or asks 'can you read this plan'. Input is analyzed in memory and never stored.

  ConnectorOAuth

• semantic_searchA

  RedM McpA

  Search RedM/RDR3 docs by behavior, concept, or token: 'teleport player', 'spawn vehicle', 'inventory add item'. Hybrid mode (default) fuses document vectors, PostgreSQL full-text results and structured discoveries records, then reranks. For a specific native use lookup_native; for ped/weapon/object/door/vehicle models use asset_lookup; for animation/audio/IMAP/clothing/wearable/particle/flag/texture records use discovery_lookup. Use grep_docs for raw references or unsupported source formats. Returns ranked snippets (path, breadcrumb, heading, snippet, score); get_document opens returned paths, including discovery:<id> records. mode=semantic searches document vectors only; mode=lexical uses lexical retrieval including structured discoveries. Filter via category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|cfx|learnings or namespace. category=cfx covers the Cfx runtime/scripting API (RegisterCommand, exports, events, statebags, fxmanifest, convars, OneSync, NUI). Community findings are merged by default; category=learnings returns only findings. If retrying after an unhelpful result, populate prior_attempt with the previous query and why it missed.

  ConnectorOAuth

• semantic_searchA

  redm-mcpA

  Search RedM/RDR3 docs by behavior, concept, or token: 'teleport player', 'spawn vehicle', 'inventory add item'. Hybrid mode (default) fuses document vectors, PostgreSQL full-text results and structured discoveries records, then reranks. For a specific native use lookup_native; for ped/weapon/object/door/vehicle models use asset_lookup; for animation/audio/IMAP/clothing/wearable/particle/flag/texture records use discovery_lookup. Use grep_docs for raw references or unsupported source formats. Returns ranked snippets (path, breadcrumb, heading, snippet, score); get_document opens returned paths, including discovery:<id> records. mode=semantic searches document vectors only; mode=lexical uses lexical retrieval including structured discoveries. Filter via category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|cfx|learnings or namespace. category=cfx covers the Cfx runtime/scripting API (RegisterCommand, exports, events, statebags, fxmanifest, convars, OneSync, NUI). Community findings are merged by default; category=learnings returns only findings. If retrying after an unhelpful result, populate prior_attempt with the previous query and why it missed.

  ConnectorOAuth

• query_sqlA

  ThinAir DataA

  Execute a read-only SQL query against the target connection. ONLY SELECT / WITH / EXPLAIN permitted. Write dialect-appropriate SQL for the connection's engine — use PostgreSQL syntax for postgres connections (`SELECT NOW()`, `LIMIT`, `ILIKE`), T-SQL for mssql (`SELECT GETDATE()`, `TOP N`, `LIKE`), MySQL for mysql (`SELECT NOW()`, `LIMIT`). Response meta includes `connection` + `dialect` so you know which syntax worked; reuse that dialect in follow-up calls. Default LIMIT 100 unless the user asks for all rows.

  ConnectorOAuth

• sixta_explain_errorA

  sixta-connectA

  Decode a database error and get the fix and the next step — no connection needed. Paste a MySQL error number (1213, 1062, 1452, 1205…) or a PostgreSQL SQLSTATE (40P01, 23505, 53300…), optionally with the failing statement, and get the proximate cause, the concrete fix, and — when it helps — the SIXTA tool and artifact to go deeper (e.g. a deadlock → paste SHOW ENGINE INNODB STATUS for sixta_explain_deadlock). Use when the user pastes a DB error code or message. Input is analyzed in memory and never stored.

  ConnectorOAuth

• read_dataA

  QuadraticA

  Read-only queries on the open spreadsheet. No data is modified. Safe to auto-approve. Call as {"action": "<name>", "params": {...}} — per-action params are listed in the Action Reference below. Special actions (not shown in the action enum): • batch — {"action": "batch", "params": {"actions": [{"action": "<name>", "params": {...}}, ...]}}. Runs reads in parallel; individual failures are reported per-entry without short-circuiting. • context — {"action": "context", "params": {"topic": "<name>"}} or {"action": "context", "params": {"action": "<name>"}}. Returns deeper docs for a topic or a single action's signature. Plural "topics" / "actions" arrays are also accepted and may be combined. Topics: python, javascript, formula, connection, validation, a1, quadratic, chart, pivot_table. Action Reference • get_cell_data(selection, page?, sheet_name?) — Returns cell values for a selection in A1 notation. Supports comma-separated ranges to fetch multiple areas in ONE call, including across different sheets. Examples: "A1:B10, D1:E10", "TableName, OtherTable", "'Sheet1'!A1:B10, 'Sheet2'!C1:D10". Table names are globally unique so they work without sheet prefixes. For cell ranges on other sheets use 'SheetName'!Range. Only use when you need the full dataset (aggregations, lookups, analysis). The file summary already includes sample rows. Results may be paginated — use page (0-based) for additional pages. • has_cell_data(selection, sheet_name?) — Check if any cells in a selection have data. Returns true if ANY cell contains data. Use before creating/moving tables or code to avoid spill errors. All ranges MUST be on the same sheet. • get_code_cell_value(code_cell_position?, code_cell_name?, sheet_name?) — Get full code from an existing Python, JavaScript, or connection code cell. Do NOT use for formula cells — formulas are already in get_cell_data results and the file summary. • get_text_formats(selection, page?, sheet_name?) — Get text formatting info. Use table column references for tables ("Table_Name[Column Name]"). Results may be paginated. • get_validations(sheet_name?) — Get all validations in a sheet. • get_conditional_formats(sheet_name) — Get all conditional formatting rules. Use to check existing rules before creating/updating/deleting. • text_search(query, case_sensitive?, whole_cell?, search_code?, regex?, sheet_name?) — Search for text in cell outputs. Supports regex when enabled (e.g., "\d+", "^hello", "foo|bar"). Searches cell outputs only, not code. Booleans default false. • get_sheet_info() — List all sheets and names. • get_spreadsheet_context(sheet_name?, include_errors?) — Full context snapshot of the file. • read_data(selection, sheet_name?, max_rows?) — Read cell data as compact CSV. Auto-tiers: returns all rows for small/medium data (<5000 rows), head+tail preview for large data. Preferred over get_cell_data for most reads. • outline(sheet_name?) — Structural map of the file: sheets, bounds, tables, code cells, charts, connections, errors. Use to understand file layout before reading data. • dependencies(position, sheet_name?, direction?) — Trace cell dependencies. direction: "forward" (what this cell reads), "reverse" (what depends on this cell), or "both" (default). • export_pdf(options?) — Export the file as a PDF with Excel-parity print semantics. Returns {mime_type, size_bytes, data_base64}. options is a camelCase object: {sheetIds?: [id], fileName?, pageSetup?: {paperSize ("letter"|"legal"|"tabloid"|"a3"|"a4"|"a5"|...), orientation ("portrait"|"landscape"), margins {left,right,top,bottom,header,footer} (inches), scaling ({type:"zoom",percent} or {type:"fitTo",width?,height?}), pageOrder ("downThenOver"|"overThenDown"), centerHorizontally?, centerVertically?, printGridlines?, printHeadings?, header/footer {odd:{left,center,right}, even?, first?} with Excel codes (&P page, &N total, &D date, &T time, &F file, &A sheet, &B bold)}, sheetOptions?: {"<sheetId>": {pageSetup?, printArea? ("A1:F20"), repeatRows? ([1,2]), repeatCols?, rowBreaks?, colBreaks?}}}. Omit options for sensible defaults (letter portrait, 100% zoom, all sheets). • list_connections(team_uuid?) — List all database connections in a team (PostgreSQL, MySQL, MS SQL, Snowflake, BigQuery, Mixpanel, Google Analytics, Plaid, etc.). Returns each connection's uuid, name, and type. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Call this BEFORE get_database_schemas or set_sql_code_cell_value to discover the connection_ids and connection types you need. • get_database_schemas(connection_ids, connection_type, team_uuid) — Get table/column schemas for database connections. Always call before writing SQL. Get connection_ids from list_connections. connection_type: POSTGRES, MYSQL, MSSQL, SNOWFLAKE, BIGQUERY, COCKROACHDB, etc. • list_agent_connections(team_uuid?) — List the team's ready Agent Connections (third-party REST API bindings). Returns each connection's uuid, name, service, base URL, auth pattern, and `{{SECRET_NAME}}` references to use in fetch code. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Reference secrets via `{{SECRET_NAME}}` in Python/JavaScript fetch code; the connection proxy substitutes team secret values at request time. • inspect_agent_connection(connection_id, team_uuid?) — Get the full schema (resources, endpoints, fields, docs URLs) and plan for one ready Agent Connection by uuid (from list_agent_connections). Call BEFORE writing fetch code against a connection so you don't guess at endpoints. team_uuid is optional with the same single-team fallback as list_agent_connections. Batch: • batch(actions) — actions: [{action, params}]. Runs reads in parallel through this same tool; per-entry failures are reported in the result without short-circuiting the batch. `action` may be any name from this reference. Nested `context` items are allowed and returned alongside the reads.

  ConnectorNo auth

• zaira_compare_toolsA

  Zaira Labs GuideA

  Compare 2-3 developer tools side by side. Returns each tool's full Markdown-KV entry separated by "===". Alternatives and worksWith are enriched with tagline + agent-readiness for resolved slugs. If any requested slugs are not found, they appear in a trailing "Note: slugs not found: ..." line; the comparison still returns for the ones found. Examples: - Three search engines: {slugs: ["meilisearch-oss", "algolia", "elasticsearch-oss"]} - Two ORMs: {slugs: ["drizzle-orm", "prisma"]} - Three auth providers: {slugs: ["auth0", "clerk", "keycloak"]} - Hosted vs self-hosted for the same vendor: {slugs: ["redis-cloud", "redis-oss"]} — shows deployment trade-off - Postgres engine vs hosted offerings: {slugs: ["postgresql", "supabase-cloud", "cockroachdb-cloud"]} Edge cases: - Cross-category comparisons (e.g., {slugs: ["auth0", "redis-cloud"]}) are allowed but rarely useful. Same-category comparisons answer "which should I pick?" better; cross-category answers "these coexist in my stack" — a compatibility question. - Minimum 2 slugs, maximum 3. Four or more is a validation error; for more, run pairs. - Invalid or unknown slugs are listed under "slugs not found"; the partial comparison returns for valid ones. - Duplicate slugs in the array are deduplicated. - A few tools are single entries (no -cloud/-oss split): stripe, auth0, firebase, twilio, openai-api, pinecone, algolia. Don't pass "stripe-cloud" — it doesn't exist. Risk: read-only, closed-world, idempotent — no state change possible.

  ConnectorNo auth

• sixta_analyze_workloadA

  sixta-connectA

  Find your worst queries by TOTAL time — no connection needed. Paste a MySQL slow query log or a PostgreSQL pg_stat_statements export and get a ranked top-N: each query shape with calls, total/mean time, and (slow log) the rows-examined-to-sent ratio, fingerprinted so thousands of log lines collapse into a few classes. Flags the dominant query, N+1 patterns, and full-scan ratios, reports how concentrated the load is (what share of total time the top shapes own), and hands the worst offenders to sixta_analyze_query. Call this whenever the user shares a slow query log or pg_stat_statements export — even a long one — or asks which queries are slowest: summing time across thousands of log lines is arithmetic a model cannot do reliably by eye. Input is analyzed in memory and never stored.

  ConnectorOAuth

• deploy_stagingA

  RationalBloksA

  Deploy a project to the staging environment. This triggers: (1) Schema validation, (2) Docker image build, (3) GitHub commit, (4) Kubernetes deployment, (5) Database migrations. The operation is ASYNCHRONOUS - it returns immediately with a job_id. Use get_job_status with the job_id to monitor progress. Deployment typically takes 2-5 minutes depending on schema complexity. If deployment fails, read the job's error first: one that starts with 'RationalBloks platform error' is the platform's, not the schema's. Otherwise check: (1) Schema format is FLAT (no 'fields' nesting), (2) Every field has a 'type' property, (3) Foreign keys reference existing tables, (4) No PostgreSQL reserved words in table/field names. Use get_project_info to see if the deployment succeeded. A deploy that drops data is refused until you pass confirm_destructive=true after reviewing the plan. One operation runs on a project at a time: while another runs, the call is refused and the refusal names the running job; wait for it with get_job_status, then call again. While RationalBloks is being updated, the call is refused with 'RationalBloks is being updated': call it again in a few minutes.

  Connector

  Destructive

  No auth

• create_userA

  mcpA

  Create a database user for a Cloud SQL instance. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * When you use the `create_user` tool, specify the type of user: `CLOUD_IAM_USER`, `CLOUD_IAM_SERVICE_ACCOUNT`, or `BUILT_IN`. * By default the newly created user is assigned the `cloudsqlsuperuser` role, unless you specify other database roles explicitly in the request. * You can use a newly created user with the `execute_sql` tool if the user is a currently logged in IAM user. The `execute_sql` tool executes the SQL statements using the privileges of the database user logged in using IAM database authentication. The `create_user` tool has the following limitations: * To create a built-in user with password, use the `password_secret_version` field to provide password using the Google Cloud Secret Manager. The value of `password_secret_version` should be the resource name of the secret version, like `projects/12345/locations/us-central1/secrets/my-password-secret/versions/1` or `projects/12345/locations/us-central1/secrets/my-password-secret/versions/latest`. The caller needs to have `secretmanager.secretVersions.access` permission on the secret version. * The `create_user` tool doesn't support creating a user for SQL Server. To create an IAM user in PostgreSQL: * The database username must be the IAM user's email address and all lowercase. For example, to create user for PostgreSQL IAM user `example-user@example.com`, you can use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance":"test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user@example.com`. To create an IAM service account in PostgreSQL: * The database username must be created without the `.gserviceaccount.com` suffix even though the full email address for the account is`service-account-name@project-id.iam.gserviceaccount.com`. For example, to create an IAM service account for PostgreSQL you can use the following request format: ``` { "name": "test@test-project.iam", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `test@test-project.iam`. To create an IAM user or IAM service account in MySQL: * When Cloud SQL for MySQL stores a username, it truncates the @ and the domain name from the user or service account's email address. For example, `example-user@example.com` becomes `example-user`. * For this reason, you can't add two IAM users or service accounts with the same username but different domain names to the same Cloud SQL instance. * For example, to create user for the MySQL IAM user `example-user@example.com`, use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user`. * For example, to create the MySQL IAM service account `service-account-name@project-id.iam.gserviceaccount.com`, use the following request: ``` { "name": "service-account-name@project-id.iam.gserviceaccount.com", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `service-account-name`.

  ConnectorNo auth

• set-envA

  mcpA

  Set an environment variable for a project. Variables are encrypted at rest (AES-256-GCM) and injected at container runtime. NOTE: DATABASE_URL, PGHOST, PGPORT, PGUSER, PGPASSWORD, and PGDATABASE are all auto-injected for the managed PostgreSQL database — you do NOT need to set any of them manually. The PORT variable is auto-managed: 8080 for auto-detected frameworks (Next.js, Node.js, Python), or auto-detected from the Dockerfile EXPOSE directive for custom Dockerfile builds. IMPORTANT: Changing env vars does NOT auto-redeploy. You must call deploy or use the redeploy API endpoint to apply changes. For Next.js apps, NEXT_PUBLIC_* variables must be set BEFORE deploying since they are embedded at build time.

  Connector

  Destructive

  No auth

• get_webmasterA

  SpytrendA

  Fetch a single webmaster (affiliate NETWORK) by UUID: aggregate stats PLUS top_creatives — the creatives this webmaster re-uploads the MOST, ordered by the webmaster's OWN in-slice ad count (slice.ads_in_slice desc — the deliberate slice order, matching what the profile page shows), media stripped/free. Each entry's total_ads is the creative's market-wide family size, folded best-effort from family redirects and floored at ads_in_slice — total_ads == ads_in_slice therefore often means 'family cards not yet built', not 'this webmaster owns the whole market'; it is context, not the sort key. analytics_pending=true means the canonical PostgreSQL identity exists but its ClickHouse aggregate is not published yet, so zero counters are not analytical zeroes. fanpages_status explicitly reports advertiser-aligned lifecycle enrichment as available, partial, unavailable or not_applicable; an omitted fanpages array is therefore never evidence that a page is alive. The id MUST be the canonical UUID returned by search_webmasters; domain names and display names are rejected instead of silently widening to the whole database. Present top_creatives as the headline final step; for the rest of the network use search_creatives?webmaster_id / search_ads?webmaster_id, and download media with get_media (entity_type=creo). identifier_inventory_semantics explains why historical page/domain/pixel inventory counts are not comparable to total_ads. attribution_link_stats.merge_edges answers WHY these identifiers sit in one network: each entry is a {page_id, page_name, domain, ads} pair — that fanpage ran exactly that many ads on that landing domain, and such a pair is what merges two networks into one (an ad whose domain belongs to network A and whose fanpage belongs to network B fuses them permanently). Weigh the edges before claiming a single operator: a 1-3 ad edge next to 100+ ad edges is a thin bridge, typically a catalog/feed ad, and is weak evidence of shared ownership; identifiers with no edge at all are inherited from earlier merges and prove nothing today. QUOTA: 1 token (one entity card). TIKTOK: with source=tiktok this fetches the TikTok cluster card instead (business-id rollup, same UUID space as search_webmasters source=tiktok results) — ⚠️ costs 100 tokens and requires a Pro-or-higher plan; the Meta-only embeds (top_creatives, attribution link-stats) are omitted on the TikTok card.

  ConnectorOAuth

• deploy_stagingA

  RationalBloksA

  Deploy a project to the staging environment. This triggers: (1) Schema validation, (2) Docker image build, (3) GitHub commit, (4) Kubernetes deployment, (5) Database migrations. The operation is ASYNCHRONOUS - it returns immediately with a job_id. Use get_job_status with the job_id to monitor progress. Deployment typically takes 2-5 minutes depending on schema complexity. If deployment fails, read the job's error first: one that starts with 'RationalBloks platform error' is the platform's, not the schema's. Otherwise check: (1) Schema format is FLAT (no 'fields' nesting), (2) Every field has a 'type' property, (3) Foreign keys reference existing tables, (4) No PostgreSQL reserved words in table/field names. Use get_project_info to see if the deployment succeeded. A deploy that drops data is refused until you pass confirm_destructive=true after reviewing the plan. One operation runs on a project at a time: while another runs, the call is refused and the refusal names the running job; wait for it with get_job_status, then call again. While RationalBloks is being updated, the call is refused with 'RationalBloks is being updated': call it again in a few minutes.

  Connector

  Destructive

  No auth

• admin_search_findingsA

  redm-mcpA

  Admin-only: rank community findings against a natural-language or token `query` using the same hybrid retrieval (vector + PostgreSQL full-text search fused via RRF, reranked) that `semantic_search` uses for the `learnings` category — but with admin fields attached: `id`, `authorIp`, `createdAt`, `linkedNatives`. Use this to find the finding an agent is complaining about, to spot near-duplicates of one before editing/merging, or to audit what a query surfaces. `admin_list_findings` is a plain ILIKE filter; this is relevance-ranked. Optional post-filters: `authorIp` (exact), `tag` (exact). `limit` default 10, max 25. Scoped to THIS deployment's game.

  ConnectorOAuth

• admin_search_findingsA

  RedM McpA

  Admin-only: rank community findings against a natural-language or token `query` using the same hybrid retrieval (vector + PostgreSQL full-text search fused via RRF, reranked) that `semantic_search` uses for the `learnings` category — but with admin fields attached: `id`, `authorIp`, `createdAt`, `linkedNatives`. Use this to find the finding an agent is complaining about, to spot near-duplicates of one before editing/merging, or to audit what a query surfaces. `admin_list_findings` is a plain ILIKE filter; this is relevance-ranked. Optional post-filters: `authorIp` (exact), `tag` (exact). `limit` default 10, max 25. Scoped to THIS deployment's game.

  ConnectorOAuth

• sixta_analyze_schema_changeA

  sixta-connectA

  Call this whenever the user proposes a migration / DDL change or asks 'is this safe to run' — before answering from memory. Whether a migration locks the table is version-specific (exactly which MySQL 8.0.x or PostgreSQL version makes an ALTER lock-free, INSTANT vs INPLACE vs COPY eligibility), and model recall of those version boundaries is unreliable — this is where answering from memory most often ships an outage. Returns an explicit safety verdict per statement (Critical/High/Medium/Info), the exact lock taken and what it blocks, the MySQL algorithm verdict with version-specific eligibility, PostgreSQL rewrite triggers, replication and MDL-starvation warnings, and the safe execution strategy (CREATE INDEX CONCURRENTLY, NOT VALID + VALIDATE, gh-ost / pt-osc) as ready-to-run SQL. Optional table size/FK/trigger hints sharpen duration estimates; for entitled Connect Pro orgs these are filled from live production context automatically (an explicit argument still wins). Findings are deterministic, treat them as ground truth. Input is analyzed in memory and never stored.

  ConnectorOAuth

• sixta_explain_deadlockA

  sixta-connectA

  Reconstruct a database deadlock from the raw dump — no connection needed. Paste the LATEST DETECTED DEADLOCK section of MySQL's SHOW ENGINE INNODB STATUS, or a PostgreSQL 'deadlock detected' log entry, and get: which transaction held and waited for which lock, the inconsistent lock-ordering that caused the cycle, which transaction was rolled back, and the consistent-ordering / short-transaction / retry fix. Use when the user pastes a deadlock dump or asks 'why did this deadlock'. Input is analyzed in memory and never stored.

  ConnectorOAuth

• create_projectA

  RationalBloksA

  Create a new RationalBloks project from a JSON schema. ⚠️ CRITICAL RULES - READ BEFORE CREATING SCHEMA: 1. FLAT FORMAT (REQUIRED): ✅ CORRECT: {users: {email: {type: "string", max_length: 255}}} ❌ WRONG: {users: {fields: {email: {type: "string"}}}} DO NOT nest under 'fields' key! 2. FIELD TYPE REQUIREMENTS: • string: MUST have "max_length" (e.g., max_length: 255) • decimal: MUST have "precision" and "scale" (e.g., precision: 10, scale: 2) • datetime: Use "datetime" NOT "timestamp" • ALL fields: MUST have "type" property 3. AUTOMATIC FIELDS (DON'T define): • id (uuid, primary key) • created_at (datetime) • updated_at (datetime) 4. USER AUTHENTICATION: ❌ NEVER create "users", "customers", "employees" tables with email/password ✅ USE built-in app_users table Example: { "employee_profiles": { "user_id": {type: "uuid", foreign_key: "app_users.id", required: true}, "department": {type: "string", max_length: 100} } } 5. AUTHORIZATION: Add user_id → app_users.id to enable "only see your own data" Example: { "orders": { "user_id": {type: "uuid", foreign_key: "app_users.id"}, "total": {type: "decimal", precision: 10, scale: 2} } } 6. FIELD OPTIONS: • required: true/false • unique: true/false • default: any value • enum: ["val1", "val2"] • foreign_key: "table.id" AVAILABLE TYPES: string, text, integer, decimal, boolean, uuid, date, datetime, json, uuid_array, integer_array, text_array, float_array Array types store PostgreSQL native arrays with automatic GIN indexing: • uuid_array: UUID[] — for sets of references (e.g., tensor coordinates) • integer_array: BIGINT[] — for dimension indices, integer sets • text_array: TEXT[] — for tags, categories, label sets • float_array: DOUBLE PRECISION[] — for weight vectors, scores GIN-indexed operators: @> (contains), <@ (contained_by), && (overlaps) BACKEND ENGINE: • python (default): FastAPI backend — mature, full-featured • rust: Axum backend — faster cold starts, lower memory, high performance WORKFLOW: 1. Use get_template_schemas FIRST to see valid examples 2. Create schema following ALL rules above 3. Call this tool (optionally choose backend_type: "python" or "rust") 4. Monitor with get_job_status (2-5 min deployment) After creation, use get_job_status with returned job_id to monitor deployment. While RationalBloks is being updated, the call is refused with 'RationalBloks is being updated': call it again in a few minutes.

  ConnectorNo auth

• search_docsA

  pg-aiguideA

  Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.

  ConnectorNo auth

• run_sqlA

  Drillr — The financial MCP for AI agentsA

  PostgreSQL SELECT over financial / market / alt-data tables — returns structured rows. Hard rules (query fails otherwise): - SELECT only, no CTE (`WITH ... AS`) — use subqueries. - Period columns are TEXT, not dates — `period_end` is 'YYYY-MM'. Compare as strings (`period_end >= '2024-01'`); a `::date` cast on it fails. - Filter structured tables by ticker (`WHERE ticker IN ('AAPL','MSFT')`; screening: add `ticker NOT LIKE '%-%'` to drop preferred stock). Tables by domain (get_table_schema gives columns + coverage note): - Market: price_volume_history (OHLCV history; MUST filter ticker + time_frame), index_price, equity_extended_rt (pre/after/overnight quotes) - Fundamentals: financial_statements (GAAP income/balance/cashflow), company_snapshot (ratios, per-share, growth) - Earnings: earning_call_summary, earning_call_calendar - Analyst: analyst_ratings, analyst_ratings_consensus - Ownership: insider_and_institution_activities - 8-K events: executive_change, company_deal_events, debt_issuance, securities_offering - Executives: executive_profile, executive_compensation - Alt-data: macro / industry / trade / AI-supply-chain — call list_tables(categories=[...])

  ConnectorNo auth

• deployA

  mcpA

  Deploy an application to sota.io. The platform auto-detects your framework and builds a Docker image automatically: - Next.js: Detected via next.config.js/ts. Add output: 'standalone' to next.config for optimal builds. - Node.js: Detected via package.json with a "start" script. Works with Express, Fastify, Koa, Hapi, etc. - Python: Detected via requirements.txt or pyproject.toml. Works with Flask, FastAPI, Django. - Custom Dockerfile: If a Dockerfile exists in the project root, it takes priority over auto-detection. Use this for Go, Rust, Java, or any other language. The EXPOSE directive in the Dockerfile is used to detect the app port automatically. THREE WAYS to supply the source code — pick EXACTLY ONE: 1. **files** (inline source from AI): Pass a map of relative paths to UTF-8 text content. Best when you've just generated a small app in this conversation and want to deploy it without any filesystem step. Up to 200 files, 10 MB total. Include the framework manifest (package.json, requirements.txt, or Dockerfile) so auto-detection works. 2. **git_url** (clone a public repo): Pass an https://, git://, ssh://, or git@host:path URL. We shallow-clone it (--depth=1 --single-branch) on the server and deploy. Optional git_branch picks a non-default branch. Only public repos are supported in v1. Max 200 MB after clone. 3. **directory** (local filesystem): Pass an absolute path. Only works when the MCP client has filesystem access (Claude Code / CLI; not Claude.ai web). Defaults to the current working directory when omitted. IMPORTANT: Your app MUST listen on the PORT environment variable. For auto-detected frameworks (Next.js, Node.js, Python) PORT is 8080. For custom Dockerfiles, the port is auto-detected from the EXPOSE directive (e.g. EXPOSE 3000 sets PORT=3000). If no EXPOSE is found, it defaults to 8080. Every project includes a managed PostgreSQL 17 database. Six environment variables are auto-injected into your container — no manual database configuration needed: DATABASE_URL (full connection string), PGHOST, PGPORT, PGUSER, PGPASSWORD, and PGDATABASE. Libraries that follow libpq conventions (node-postgres, pgx, psycopg2, Django) pick up the PG* variables automatically with no configuration. If your app needs database migrations, run them on startup. Deployments use blue-green strategy for zero downtime. The old container keeps running until the new one passes health checks (60s timeout). Use get-logs to monitor build progress. Files matching .gitignore, .git/, node_modules/, .env, and .DS_Store are excluded from the archive.

  Connector

  Destructive

  No auth