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458,158 tools. Updated 2026-08-14 23:05

"A server for finding test-related information" matching MCP tools:

  • List the public disclosure feeds this server aggregates, how many disclosures are cached per source, each source's newest item and an honest staleness flag, plus cache ages. Takes no arguments. Also states the scope plainly: public feeds only — no .onion access, no arbitrary fetching or crawling, no credential or PII output. Check this first if another tool's answer looks thin: a stale live feed is a finding, not background noise.
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  • Connection test / health check — call this first to confirm the server is reachable. Returns server identity, deploy version, tool count, station coverage, and the update times of the realtime layers (JMA alerts, train status) so you can confirm freshness, not just liveness. No auth, no arguments, lightweight.
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  • Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.
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  • Hiring velocity across tracked Bitcoin and crypto-infrastructure employers, counted from their live ATS boards. Returns { as_of, companies[], note, why, disclaimer }; each company carries company, ticker, category, ats, careers_url, open_roles, open_roles_30d_ago, open_roles_90d_ago and the derived delta_30d, delta_90d and pct_30d. Example: {"company": "coinbase"} for one employer, or {} for every employer tracked. When a company filter matches no tracked employer the response adds coverage_note and tracked_count, saying that the name is outside the tracked set — a limit of coverage, not a finding about whether that company is hiring. Information, not financial advice.
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  • Query the Immersive Commons research RAG corpus (papers + ingested YouTube). Returns top-k chunks with similarity scores and source links. The query text is forwarded to a server-side RAG proxy (supercommons2 via Tailnet Funnel) and NEVER logged on the IC side — privacy contract. Use this for literature lookups, finding related work, surfacing citations the floor has already ingested. Args: { question: string (<=500 chars), k?: number (1-50, default 10), sources?: ('paper'|'book')[] (default ['paper']) }. Returns the upstream RAG response shape — typically { results: [{ paper_id, title, similarity, snippet, link }, ...] }. Required scope: research:query.
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  • Get care plan material for a specific NANDA-style nursing diagnosis: its definition, related factors (the "related to" clause), defining characteristics (the "as evidenced by" clause), SMART goals, interventions, and the conditions where it is a priority. Use when a nursing student asks about a diagnosis rather than a disease, for example "risk for infection", "acute pain", "impaired gas exchange", "ineffective coping" or "risk for falls", or asks how to write a three-part diagnosis or an AEB statement. Educational reference, not medical advice.
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  • Run a source-free compiler smoke test through the real Axint pipeline. Use immediately after installing or connecting Axint so the current agent proves it did more than start the MCP server. Use: call immediately after install or first MCP connection; use validate or run for project checks. Inputs: format changes rendering only; the smoke test has no project inputs. Effects: read-only built-in compiler smoke test; writes no files and uses no network.
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  • Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.
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  • Produce a deterministic remediation REQUEST bundle (rubric + fix schema + per-finding metadata + fingerprints) for YOU (the host agent) to fix. This tool calls no model and needs no key. For each finding, propose the corrected FULL file content, then VERIFY with verify_fix and keep only fixes that clear the finding. Never touch files with secrets; never auto-merge. Pass 'findings' from scan_path --format json.
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  • Hiring velocity across tracked Bitcoin and crypto-infrastructure employers, counted from their live ATS boards. Returns { as_of, companies[], note, why, disclaimer }; each company carries company, ticker, category, ats, careers_url, open_roles, open_roles_30d_ago, open_roles_90d_ago and the derived delta_30d, delta_90d and pct_30d. Example: {"company": "coinbase"} for one employer, or {} for every employer tracked. When a company filter matches no tracked employer the response adds coverage_note and tracked_count, saying that the name is outside the tracked set — a limit of coverage, not a finding about whether that company is hiring. Information, not financial advice.
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  • Quick company lookup: facilities (with addresses and operations) and enforcement actions (recalls) for a single company and its known aliases. Costs 1 credit. Excludes: 510(k) clearances, PMA approvals, drug applications, inspection history, and subsidiary data. Related: fda_company_full (adds clearances/approvals/drugs for 5 credits), fda_suggest_subsidiaries (discover related entities), fda_get_facility (per-facility products and operations by FEI).
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  • Get Lenny Zeltser's one-page Vulnerability Advisory Brief template. Covers Bottom Line, Quick Facts, Are We Affected?, Defensive Actions (with What/Why/When/Who), What We Don't Know, and More Information. This server never requests your vulnerability notes and instructs your AI to keep them local—the brief template and guidelines flow to your AI for local analysis.
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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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  • Analyze a server response from authorized probing for information leakage, fingerprinting signals, and exploitation opportunities scoped to the authorized engagement. Accepts raw HTTP response headers and body (paste from Burp, curl, or any HTTP client) and returns structured findings grouped by category. Each finding includes: what was detected, why it matters for an authorized tester, how a defender detects misuse, and recommended remediation. Identifies version disclosures, stack traces, debug headers, internal paths, JWT/cookie patterns, CORS misconfigurations, and other common leakage patterns. Use as the bridge between recon/probing output and the methodology and payload tools.
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  • List EVERY test inside a TestRail run, walking all pages server-side so the caller never handles offsets. Returns each test with its T-id (test instance, e.g. T1234), C-id (underlying case_id — feed this to testrail_get_case), title, and status_id. Filter by statusIds (TestRail status: 1=passed, 2=blocked, 3=untested, 4=retest, 5=failed) to e.g. list only failed tests when triaging. Capped at 20 pages (5000 tests); check capReached before concluding a test is not in the run.
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  • SESSION Management ($0.05). Generates a new `pipeline_id` and returns session metadata (scope, expiry, initial drift_mode) for organizing a series of related checks under one identifier. Note: this call does not currently link the returned pipeline_id to later evaluate_* calls — there is no server-side session state that ties subsequent audits back to it; it is an identifier/timestamp issuer, not an active tracking session. Use this to obtain a shared reference ID for your own client-side grouping of a multi-step audit sequence; do not rely on it to automatically aggregate drift across calls.
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  • Generate a cited, runnable conformance test that verifies one normative requirement. The test is grounded in the requirement sentence plus its surrounding clause from the corpus, and every test cites the spec, section and page it enforces. If the requirement is not testable (boilerplate, scope text), the generator abstains rather than inventing a test. Args: requirement_id: The requirement's id, as shown by get_requirements target: "pytest" for a runnable Python test module, or "gherkin" for a reviewable .feature file (default: pytest)
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  • Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.
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  • Get information about related addresses of an input address. Note: This only includes the the "special" connections 'First Funder', 'Signer', 'Previous Signer', 'Multisig Signer of', 'Previous Multisig Signer of', 'Deployed via', 'Deployed by', 'Deployed Contract', 'Created Contract', 'Created by'. To get related wallets, also check address counterparties. First funder exchange withdrawal address does usually NOT belong to the same entity as the address, only deposit addresses. Only information is that it has been funded by the exchange.
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  • General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)
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