Skip to main content
Glama
744,934 tools. Updated 2026-11-05 01:14

"A database management system for handling and organizing data" matching MCP tools:

  • Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.
    ConnectorNo auth
  • Returns the complete surveillance intelligence record for a domain name. If the domain is in TunnelMind's tracker database (80,000+ entries), the response includes tracker category, risk score, fingerprinting data, cookie persistence, IAB TCF purposes, and the owning corporate entity. If the domain is not in the database, a live probe is automatically run: RDAP registration data, DNS records (MX, SPF, TXT verification tokens), HTTP headers, and CSP third-party actors are fetched fresh from the edge and returned. Use this tool when: - You need to know whether a specific domain tracks users, and how aggressively. - You are researching who owns a domain and what corporate entity controls it. - You want to check HTTP security headers and third-party services embedded in a site. - You are building a risk score for a domain before routing traffic through it. Do NOT use this tool when: - You want to search by keyword or category — use `search` instead. - You want all domains for an entity — use `get_entity` instead. Inputs: - `domain` (path, required): Domain name. Strip `www.` prefix — it is removed automatically. Subdomains are resolved to the parent: `ads.doubleclick.net` → `doubleclick.net`. Examples: `doubleclick.net`, `google-analytics.com`, `intercom.io`. Returns: - Full `DomainRecord`. Free tier returns the domain, category, score, prevalence, and entity name. Pro/enterprise additionally return `tcf_vendor_id`, `tcf_purposes`, `tcf_features`, and `disconnect_cats`. - If the domain is not in the tracker database, `live_lookup: true` is set and RDAP/DNS/HTTP probe results are returned instead of tracker fields. - 404 if the domain cannot be found via live probe either (unknown TLD, unreachable). Cost: - Anonymous (no key): 10/min, 100/day. Free key: 50/day. Pro/enterprise: included in plan. Latency: - Database hit: typical <100ms, p99 <300ms. - Live probe: typical 2-5s, p99 10s (external DNS/HTTP calls).
    ConnectorNo auth
  • Golden Alerts permanent monthly archive — Returns the permanent monthly archive of Golden Alert activity — one row per calendar month, aggregated from daily snapshots before they are purged. This archive is never deleted and grows indefinitely, providing AI agents with long-term trend data on alert severity and top tokens across months and years. Each month includes: totalCount (total alerts that month), highCount/mediumCount/lowCount (severity breakdown), topTokens (5 most-active tokens), daysInMonth (days with data), avgPerDay (daily average). Months with fewer than 20 daily records are excluded to ensure statistical accuracy. Data source: CryptoWhaleInsights own signal_history database (49,000+ on-chain signals). No authentication required. 60 req/min. 5-min cache. — Use this for long-term monthly archive data; use the corresponding live or daily-history tool for current or finer-grained data.
    ConnectorNo auth
  • Run a read-only SQL query against an app's Postgres database and return up to 200 result rows. SELECT only — writes and DDL (INSERT/UPDATE/DELETE/ALTER/DROP/…) are rejected server-side; use vibekit_chat or vibekit_submit_task to have the agent make data or schema changes. Call vibekit_db_schema first to learn the tables. SQL string, max 5000 chars.
    ConnectorNo auth
  • Change what a site's docroot IS, without deleting anything. Types: wordpress WordPress, PHP and a database (the default) php PHP and a database, no WordPress php-nodb PHP, no database static-site HTML/CSS/JS only, no PHP and no database ⚠ NON-DESTRUCTIVE. Existing files stay on disk and existing databases are not dropped. They remain the customer's data and keep counting against their plan's quota — this changes how the site is SERVED, not what it holds. Converting back later finds everything where it was. ⚠ Moving to a type without PHP does not merely stop executing .php — it stops serving them. The vhost returns 404 for php/phtml, because falling through to the static handler would return the file's SOURCE, and a docroot converted from WordPress still contains wp-config.php with the database password in it. Shared hosting only. On a VPS the whole container is the customer's and they reconfigure it with the tools inside it. Requires: API key with write scope. Args: slug: Site identifier site_type: One of the four values above Returns: {"from": "wordpress", "to": "static-site", "database_created": false, "placeholder_seeded": false, "pool_removed": true} `placeholder_seeded` is true only when the docroot was EMPTY — an existing site's content is never overwritten. Errors: VALIDATION_ERROR: Unknown type, or the site is not on shared hosting NOT_FOUND: Unknown slug or not reachable by this account
    ConnectorNo auth
  • Checks source you already generated against the secureFlows integration rules. Needs no secureFlows token; safe at scaffolding time. Pass every auth/session-related file in one call — some checks are evaluated across the whole set. Two kinds of findings: • scope "file" — a forbidden construct is present (localStorage token, legacy /app/login, fetch-based logout, client-side JWT decode, empty catch, restore non-auth errors clearing session UI, Continue CTA gated on null session, ...), reported at an exact file:line. • scope "project" — REQUIRED handling is missing everywhere you passed in: detecting 401/410 but never clearing the token, never handling 403, or handling 403 without the BILLING_GRACE_LOCK carve-out. These are the defects that actually dominate real generated apps, and no "forbidden pattern" check can see them, because the bug is an absence. Heuristic text analysis, not a parser or a type checker. It can miss things it has no rule for, and a project check can be satisfied by the right keyword in the wrong place. It is a fast first pass — not a substitute for the Agent implementation checklist in SKILL.md, and specifically not for the checks that need a running app (auth-guard mount races, the fresh-reload check). Fix every "error" before calling an integration done; treat "needs_review" as a lead.
    ConnectorNo auth

Matching MCP Servers

Matching MCP Connectors

  • Checks source you already generated against the secureFlows integration rules. Needs no secureFlows token; safe at scaffolding time. Pass every auth/session-related file in one call — some checks are evaluated across the whole set. Two kinds of findings: • scope "file" — a forbidden construct is present (localStorage token, legacy /app/login, fetch-based logout, client-side JWT decode, empty catch, restore non-auth errors clearing session UI, Continue CTA gated on null session, ...), reported at an exact file:line. • scope "project" — REQUIRED handling is missing everywhere you passed in: detecting 401/410 but never clearing the token, never handling 403, or handling 403 without the BILLING_GRACE_LOCK carve-out. These are the defects that actually dominate real generated apps, and no "forbidden pattern" check can see them, because the bug is an absence. Heuristic text analysis, not a parser or a type checker. It can miss things it has no rule for, and a project check can be satisfied by the right keyword in the wrong place. It is a fast first pass — not a substitute for the Agent implementation checklist in SKILL.md, and specifically not for the checks that need a running app (auth-guard mount races, the fresh-reload check). Fix every "error" before calling an integration done; treat "needs_review" as a lead.
    ConnectorNo auth
  • Mint a PROJECT-scoped management token (`er_mcp_`) for MCP and REST; it cannot authenticate relay traffic. Use it after create_project to configure a fresh project, or for any project you already own. Attenuated by design: the scopes must be a subset of THIS management token's own grant (`read` is always included), expiry is mandatory (1–90 days, default 30, never "never"), and the minted management token (being project-scoped) can never mint management tokens itself. `spend` is human-granted only: no management token, of any scope, can mint one carrying it. Mint a spend-scoped token from the project's panel instead. Requires an ACCOUNT-scoped management token and the `config` scope. Returns the plaintext exactly once; only its hash is stored.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Search posts by keywords. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: sentiment analysis, trend detection, content analysis across thousands of posts. Returns by default: id, text, authorUsername, createdAtDate. First searches database, then external API if data is stale or missing. NOT for URL lookups - use getTwitterPostsByIds. QUERY SYNTAX: Plain keywords (bitcoin, climate change), quoted phrases ("deep learning"), boolean expressions (AI AND crypto, bitcoin OR ethereum, politics NOT sports), or parenthesized groups ((startup OR entrepreneur) NOT "venture capital"). AND/OR/NOT must have a term on both sides. @handles like @karpathy are supported. Field operators (from:, lang:) are stripped. Forward slashes are treated as spaces (24/7 becomes 24 7). SORT: sortBy="relevance" (default, best matches first) or sortBy="latest" (newest first). Filters: language, authorId/authorUsername, countryCode (2-letter ISO code, feature-gated - only accounts with the country filter enabled; filters by the tagged place country in both database and on-demand results). Date filters: OMIT startDate/endDate by default. ONLY pass if user explicitly requests specific date range (YYYY-MM-DD format). Use filterOutRetweets=true to exclude retweets. IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FIELDS parameter (optional): Specify to get additional/different fields. Available: Core (id, text, authorId, authorUsername, createdAt), Engagement (retweetCount, replyCount, quoteCount, impressionCount, bookmarkCount), Metadata (lang, source, suspended, deleted), Relations (conversationId, quotedTweetId, retweetedTweetId, replyToTweetId, replyToUserId, replyToUsername), Content (hashtags, mentions, mediaUrls), Location (placeName, placeCountry, placeCountryCode, placeBoundingBoxCoordinates, placeCentroid). This is a safe, read-only tool for analyzing searchable information.
    ConnectorOAuth
  • Get retweets of specific post. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Database-only. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. Database-only search for historical retweet data. Date filter: OMIT startDate by default. ONLY pass if user explicitly requests filtering from specific date (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Use to analyze post amplification patterns. NOT for quotes - use getTwitterPostQuotes. Optional fields parameter for performance: ["id", "authorUsername", "createdAt"]. This is a safe, read-only tool for analyzing searchable information.
    ConnectorOAuth
  • Run ONE bounded read against a site's database. One statement, beginning with SELECT, SHOW, DESCRIBE or EXPLAIN, with no second statement and no data-modifying clause anywhere in it. The container proves the statement is a read before running it and caps the result: 200 rows by default, 1000 maximum, 2 MB of cell data. `truncated` in the response says whether a cap was reached. ⚠ THIS TOOL CANNOT CHANGE ANYTHING, AND RETRYING WITH DIFFERENT WORDING WILL NOT MAKE IT. Anything that is not a single bounded read is refused with READ_ONLY and nothing runs. To change data or schema, use the tool for the job: optimize_database, database_search_replace, manage_db_user, list_databases, list_tables — and for creating, altering, dropping, importing or exporting tables, the database manager in the control panel, which has no tool here. Requires: API key with write scope (unchanged — the scope is the customer's published permission for this operation, not a claim about what it does). Args: slug: Site identifier database: Database name query: One read statement Returns: {"columns": ["id", "user_email"], "rows": [[1, "a@example.com"], ...], "row_count": 1, "truncated": false, "execution_time_ms": 12.0} Errors: READ_ONLY: Not a single bounded read. The error text carries the explanation and names the operation to use instead.
    ConnectorNo auth
  • 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
  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
    ConnectorNo auth
  • List the standardized conditions the Veterans’ Rights corpus organizes Board of Veterans’ Appeals decisions under, each with the number of vetted decisions, grants, and remands (full-corpus counts) and the body-system category. Use this to discover which conditions have data, to get the exact slug for get_condition_report, or to see which conditions occur in this collection. Counts describe whole decisions mentioning each condition, not condition-specific wins or individual chances. Counts reflect only quality-filtered, publicly visible decisions.
    ConnectorNo auth
  • Run a READ-ONLY SQL query against the project's Postgres database (SELECT, EXPLAIN, etc.) — the Floot-managed database, or an external Postgres the user connected to the project. Writes are rejected — use execute_sql for those. Returns JSON: `{rows, rowCount, command, truncated?}` (or `{results: [...]}` for multi-statement queries). Pass `database` only if the project has more than one.
    ConnectorOAuth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth
  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.
    ConnectorNo auth