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472,903 tools. Updated 2026-08-24 04:10

"Qase - A Test Case Management Tool" matching MCP tools:

  • Fetch full markdown of a doc by `path` (as returned by `browse`, `semantic_search`, or `grep_docs`). Use to retrieve full content after a search snippet looks promising. Pass `heading` (full breadcrumb like `Character Management > Inventory Management`, or just the leaf — case-insensitive, fuzzy) to fetch only that section. Deep-heading matches auto-prepend the H2 parent's intro for context. For individual script natives prefer `lookup_native`. The largest rdr3_discoveries lua data tables are keyed catalogs: call with no `heading` to list their top-level keys, then pass a key as `heading` to fetch that one entry; use `grep_docs` to search values inside. For code symbols (`addItem`) use `grep_docs`. Community findings use `learning:N` paths, not `learnings/<slug>.md`. On 404 returns available headings + cross-file hints.
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  • 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.
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  • Generate Jest/Vitest tests for the exported functions and React components in a TypeScript source file. Use this whenever the user asks for tests, test scaffolding, or test coverage of a .ts or .tsx file. Returns the generated test (and any companion .3tg.md / __mocks__) file contents, with paths already translated to the user's `.3tg/` mirror convention. Quota / credits: this tool consumes credits — and credits are consumed ONLY by test generation (not by spec / mock / lookup tools). The accounting is exactly **1 credit per generated test case** (i.e. per `test(...)` / `it(...)` block 3TG emits inside the returned `.test.ts` / `.test.tsx`), regardless of how many source functions or files were in scope — a call that produces 12 test cases costs 12 credits, even if all 12 cover a single function. Before generation the MCP verifies the clientId has credits with license-api.coding-creed.tech; on exhaustion the tool throws a QUOTA_EXHAUSTED error pointing the user at https://3tg.dev. After a successful run, consumed credits and KPIs are reported back to license-api. Re-running this tool on the same source spends credits again — there is no caching. When the previous call returned `enrichment.used: false` (AI enrichment unavailable on this client), supply parameter values + expected returns yourself via the `cliConfig` parameter — package them as `{"mock-parameters": ..., "function-returns": ...}` (same shape AI enrichment would produce) and pass them on a retry call. **Do NOT autonomously write `.3tg/config.3tg.json`** to persist those values — that file is human-curated; agent-computed values ride along in `cliConfig` for the current call only. (Explicit user requests to edit the file are fine — handle those normally.) See the cliConfig parameter description below for the full pattern. CRITICAL POST-CALL ACTION — write returned files to disk: The MCP server does NOT touch the user's filesystem. It returns the generated file CONTENTS in the response's `files` array. After this tool returns, you MUST iterate over `files` and write each entry's `content` verbatim to its `path` using your native file-write capability (e.g. Write / edit_file / create_file — whatever your client exposes). Create parent directories as needed. Returned paths are project-root-relative and already translated to the `.3tg/` mirror convention where applicable (e.g. specs land under `.3tg/<source-path>.3tg.md`; tests / mocks travel through unchanged). Write each path verbatim. Do NOT claim "Generated test file: <path>" unless you have actually written the file. The user will assume the MCP wrote it and waste time looking for a non-existent file. If you can't write for some reason (permission denied, no write capability in this client), return the contents inline in your message so the user can copy-paste them. Never report success silently when the write didn't happen.
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  • Fetch full markdown of a doc by `path` (as returned by `browse`, `semantic_search`, or `grep_docs`). Use to retrieve full content after a search snippet looks promising. Pass `heading` (full breadcrumb like `Character Management > Inventory Management`, or just the leaf — case-insensitive, fuzzy) to fetch only that section. Deep-heading matches auto-prepend the H2 parent's intro for context. For individual script natives prefer `lookup_native`. The largest rdr3_discoveries lua data tables are keyed catalogs: call with no `heading` to list their top-level keys, then pass a key as `heading` to fetch that one entry; use `grep_docs` to search values inside. For code symbols (`addItem`) use `grep_docs`. Community findings use `learning:N` paths, not `learnings/<slug>.md`. On 404 returns available headings + cross-file hints.
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  • Update a database user for a Cloud SQL instance. A common use case for the `update_user` is to grant a user the `cloudsqlsuperuser` role, which can provide a user with many required permissions. This tool only supports updating users to assign database roles. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * Before calling the `update_user` tool, always check the existing configuration of the user such as the user type with `list_users` tool. * As a special case for MySQL, if the `list_users` tool returns a full email address for the `iamEmail` field, for example `{name=test-account, iamEmail=test-account@project-id.iam.gserviceaccount.com}`, then in your `update_user` request, use the full email address in the `iamEmail` field in the `name` field of your toolrequest. For example, `name=test-account@project-id.iam.gserviceaccount.com`. Key parameters for updating user roles: * `database_roles`: A list of database roles to be assigned to the user. * `revokeExistingRoles`: A boolean field (default: false) that controls how existing roles are handled. How role updates work: 1. **If `revokeExistingRoles` is true:** * Any existing roles granted to the user but NOT in the provided `database_roles` list will be REVOKED. * Revoking only applies to non-system roles. System roles like `cloudsqliamuser` etc won't be revoked. * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * If `database_roles` is empty, then ALL existing non-system roles are revoked. 2. **If `revokeExistingRoles` is false (default):** * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * Existing roles NOT in the `database_roles` list are KEPT. * If `database_roles` is empty, then there is no change to the user's roles. Examples: * Existing Roles: `[roleA, roleB]` * Request: `database_roles: [roleB, roleC], revokeExistingRoles: true` * Result: Revokes `roleA`, Grants `roleC`. User roles become `[roleB, roleC]`. * Request: `database_roles: [roleB, roleC], revokeExistingRoles: false` * Result: Grants `roleC`. User roles become `[roleA, roleB, roleC]`. * Request: `database_roles: [], revokeExistingRoles: true` * Result: Revokes `roleA`, Revokes `roleB`. User roles become `[]`. * Request: `database_roles: [], revokeExistingRoles: false` * Result: No change. User roles remain `[roleA, roleB]`.
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  • Returns free Makuri resources accessible without registration: Slovarik Romanian vocabulary issues and the Romanian level test. Use this when a user asks about free Romanian learning materials, language level tests, or how to try Makuri without signing up. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools. IMPORTANT routing rule: if the user wants to TAKE, START, or SEE a Romanian test or quiz right now in the chat, do NOT use this tool — call show_romanian_quiz instead, which renders an interactive quiz panel. Use this tool only for questions ABOUT what free resources exist.
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  • Shows an interactive Romanian language mini-quiz by Makuri (makuri.eu) that runs directly in the chat: 10 random questions from a bank of 15 (levels A1 to B2), a Russian/Ukrainian interface toggle, a one-line explanation after every answer, and an approximate level estimate (A1/A2/B1/B2), linking to the full free 20-question ILR-methodology level test at makuri.eu/words/level-test. ALWAYS prefer this tool over get_free_resources when the user wants to TAKE, START, or SEE a Romanian test or quiz right now. Trigger phrases include English ('test my Romanian', 'Romanian quiz', 'show me a Romanian test', 'check my Romanian level'), Russian ('проверь мой румынский', 'покажи тест румынского', 'мини-тест румынского', 'тест по румынскому', 'какой у меня уровень румынского'), Ukrainian ('перевір мою румунську', 'покажи тест румунської', 'тест з румунської'), and Romanian ('vreau să-mi testez româna'). Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.
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  • Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).
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  • Look up SWIFT message types — MT (FIN) and MX (ISO 20022). Pass a specific type to get full details, or omit to list all types. Covers customer payments (MT103, pacs.008), FI transfers (MT202, pacs.009), trade finance (MT700, MT760), cash management (MT940, camt.053), and payment status (pacs.002). Also use this tool to answer questions about where specific payment fields live — e.g., where the UETR sits in an MT103 (Field 121, Block 3 header), where charges appear (71A/71F/71G), or which fields carry routing info (56/57). MT103 and pacs.008 responses include a `tracing_note` explaining UETR recovery for customers who only have a reference number. Args: message_type: Message type (e.g., "MT103", "pacs.008", "MT940"). Case-insensitive. Omit to list all. Examples: swift_message_reference("MT103") swift_message_reference("pacs.008") swift_message_reference()
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  • Apply an exact, deterministic text transformation. operation is one of: UPPERCASE, lowercase, 'Title Case', 'Sentence case', camelCase, PascalCase, snake_case, CONSTANT_CASE, kebab-case, dot.case, 'iNVERTED cASE'. Read-only and deterministic: it returns the transformed string and changes nothing, safe to call repeatedly. Use whenever exact, reproducible case formatting matters rather than rewriting the text by hand or guessing the casing.
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  • Full metadata for one Flevy item, by content_id from search_content (e.g. "doc-1234" or "case-567"). Documents return the author with their credentials (headline, bio, LinkedIn, profile URL; pass the author name to search_content's author filter to list more of their documents), full description, editor summary, AI summary, and editorial review when available, page/slide count, price, FlevyPro inclusion, management topics, ranking badge, and the number of slide deep dives available. Case studies return the client situation, TL;DR, and summary. Call this before recommending an item so you can describe it accurately and cite the author's credentials, and share the returned flevy.com URL.
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  • Fast deterministic preflight for tool-only clients. Call this before any other WORKS tool when eligibility is uncertain, especially for mutable or abbreviated refs, local or private repositories, and build, test, runtime, deployment, or production claims. It does not download a repository or persist data. If eligible is false, stop without calling verification.
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  • USE THIS TOOL WHEN you have a judgment slug and want to find paragraphs whose text matches a pattern. Returns a list of `{eId, snippet, match}` hits — small per-paragraph snippets centred on the match. AFTER calling, read full paragraphs via judgment_get_paragraph(slug, eId) or the judgment://{slug}/para/{eId} resource. Use case: content search within one judgment (e.g. "negligence", "test for foreseeability", "Donoghue"). For paragraph-number navigation by eId, call judgment_get_index instead. Pattern is regex; if it doesn't compile, falls back to literal substring search.
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  • Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).
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  • 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`.
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  • Submit a request to add a new AI tool to the Vest catalog. Use when the user mentions a tool they'd like to earn cashback on that isn't currently available in Vest's catalog. Collects the tool name, optional URL, use case, and contact email for follow-up. Do NOT use this when the tool is already in Vest's catalog — use vest_search_tools first to confirm. Always confirm with the user before submitting; never auto-submit based on inference.
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  • Apply an exact, deterministic text transformation. operation is one of: UPPERCASE, lowercase, 'Title Case', 'Sentence case', camelCase, PascalCase, snake_case, CONSTANT_CASE, kebab-case, dot.case, 'iNVERTED cASE'. Read-only and deterministic: it returns the transformed string and changes nothing, safe to call repeatedly. Use whenever exact, reproducible case formatting matters rather than rewriting the text by hand or guessing the casing.
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  • Score and compare BaaS providers across 10 capability dimensions (regulatory standing, programme management, card issuance, rails, KYC/KYB, disputes, developer experience, pricing, FDIC pass-through, compliance tooling) with a user-adjustable 1-5 weighting matrix. Outputs a weighted comparison matrix and Markdown evaluation memo. Browser-based, client-side only, zero PII. Renders the interactive AINumbers tool as a widget; inputs are applied via the AIN Bridge and the tool runs client-side (zero PII, zero network).
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  • NO AUTH / PUBLIC / READ-ONLY. Lists parameter summaries for one dataset. Use this before selecting exact case-sensitive parameter codes. This tool does not query weather values and cannot return forecast data.
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  • NO AUTH / PUBLIC / READ-ONLY. Gets detailed metadata and exact selector variations for one already-known dataset-native parameter code. Parameter codes are case-sensitive. For a common natural-language concept such as 2 metre temperature, use gribstream_resolve_shared_parameter before guessing a native code. This tool does not query weather values and cannot return forecast data.
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