135,023 tools. Last updated 2026-05-16 13:58
"A CLI tool for auditing MCP (Model Context Protocol) configurations or servers" matching MCP tools:
- Fetches an AI-synthesised Moon-sign horoscope for a chosen horizon and returns structured guidance fields plus metadata about the model and period. SECTION: WHAT THIS TOOL COVERS Calls the upstream horoscope service for a lunar sign (English or Sanskrit input accepted; response normalises moon_sign to lowercase English) and a period of daily, weekly, monthly, or yearly. It returns narrative and checklist-style content for life areas, remedy, and timing flavour text. It does not compute a personal natal chart, divisional charts, or dasha — only sign-level transit-flavoured copy tied to the requested horizon. SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_natal_chart — if the user needs a personalised chart beyond sign-general copy. SECTION: INPUT CONTRACT period is constrained to the tool schema enum (daily, weekly, monthly, yearly). moon_sign accepts Sanskrit (Tula, Vrischika, Karka, Simha, Kanya, Dhanu, Makara, Kumbha, Meena, Mesha, Vrishabha, Mithuna) or English (Libra, Scorpio, Cancer, Leo, Virgo, Sagittarius, Capricorn, Aquarius, Pisces, Aries, Taurus, Gemini); resolution is upstream. response_format selects JSON vs markdown rendering only. SECTION: OUTPUT CONTRACT data.content: do[] (string array) body (string) love (string) avoid[] (string array) money (string) career (string) remedy (string) headline (string) narrative (string) open_loop (string) data.model_used (string — AI model version label) data.generated_at (string — ISO UTC) data.period_key (string — YYYY-MM-DD for daily; identifier for other horizons) data.horizon (string — 'daily', 'weekly', 'monthly', or 'yearly') data.moon_sign (string — lowercase English, e.g. 'libra') SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — Invalid period enum or other Pydantic field violations on the tool schema → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — Unknown or unsupported moon_sign → MCP INTERNAL_ERROR at the tool layer (upstream rejection). INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Sign-level content only; not a substitute for birth-chart analysis. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — full personalised sidereal chart from birth data, not Moon-sign editorial copy. asterwise_get_gochar — nine-planet transit snapshot vs natal chart for today, not AI horoscope prose.Connector
- Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).Connector
- [IN DEVELOPMENT] [READ] Search the Layer 3 curated directory of MCP servers and agent-work tools. The directory has 30 entries across three vetting tiers — `first-party` (operated by the swarm.tips DAO), `vetted` (third-party, we've used + verified), `discovered` (cataloged from public sources, not yet exercised). Filter by `query` (substring vs name/description/tags), `category` (substring), and `tier`. Results sort first-party → vetted → discovered. The same directory powers swarm.tips/discover; this tool exposes it programmatically. Use this when an agent needs to find an MCP server for a capability (DeFi, search, browser automation, etc.) instead of an opportunity (which `discover_opportunities` covers).Connector
- Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")Connector
- Return a curated snapshot of currently-live audit competitions and bug-bounty programs across Code4rena, Cantina, Sherlock, and direct-protocol channels. Useful for solo wardens triaging which contests to enter. Snapshot updates with each cipher-x402-mcp release; treat the data as a hint, always cross-check the platform before submitting. Free, no payment required.Connector
- Browse and compare Licium's agents and tools. Use this when you want to SEE what's available before executing. WHAT YOU CAN DO: - Search tools: "email sending MCP servers" → finds matching tools with reputation scores - Search agents: "FDA analysis agents" → finds specialist agents with success rates - Compare: "agents for code review" → ranked by reputation, shows pricing - Check status: "is resend-mcp working?" → health check on specific tool/agent - Find alternatives: "alternatives to X that failed" → backup options WHEN TO USE: When you want to browse, compare, or check before executing. If you just want results, use licium instead.Connector
Matching MCP Servers
- Alicense-qualityDmaintenanceProvides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.Last updated2MIT
- Alicense-qualityCmaintenanceA demonstration server for the Model Context Protocol (MCP) that exposes calculator and Yahoo Finance tools, allowing LLMs to interpret natural language requests and make tool calls via the MCP standard.Last updated1Apache 2.0
Matching MCP Connectors
Zero-value tracer token system that tracks AI agent activity across the internet. Agents earn tokens by submitting threat intelligence traces, with free trust verification (verify_trust) and paid threat intelligence feeds. 8 tools: submit_trace, check_token_balance, mutate_token, get_trace_schema, verify_trust (free) + threat_intelligence_feed, bulk_verify_trust, query_trace_analytics (paid).
Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.
- Atomic test set + cases + mocks + mappings ingest. Creates the test set row, every test case, every mock, and the mapping doc in one call. PREFER THE CLI FOR ON-DISK RECORDINGS. When the dev has a recorded test-set on disk (e.g. `./keploy/test-set-0/` produced by `keploy record`), invoke this via Bash instead — it streams bytes from disk to server in one HTTP round-trip: ``` keploy upload test-set \ --app <namespace.deployment> # or --cloud-app-id <uuid> --branch <uuid|name> # optional, find-or-create on name --test-set <path|name> # e.g. keploy/test-set-0 [--name <override>] # rename on the server ``` The CLI path runs in ~3 seconds for a typical recording; calling this MCP tool directly with the same bundle inlined as args takes minutes because Claude has to serialize ~10K+ tokens of YAML/JSON through tool_use. Reserve this MCP tool for cases where the data is already in conversation context (e.g. you just generated test cases programmatically and don't want to round-trip to disk). Each step is its own DB write; partial failure leaves earlier rows in place — callers can replay safely. `branch_id` is REQUIRED — direct writes to main via MCP are blocked. Every row lands on the branch overlay until merge. `test_cases[].mock_names` lists the mocks each case consumes; the server folds these into the mapping doc on upload. Returns { test_set, test_case_ids, mock_ids }.Connector
- Initialize a persistent memory context for a conversation or agent session.Connector
- Fetches the specific deposit address for the TronSave internal account. Requires a logged-in MCP session created by the `tronsave_login` tool: include `mcp-session-id: <sessionId>` returned by `tronsave_login` on subsequent MCP requests. Internal tools never accept API keys via tool arguments; signature sessions resolve the latest internal API key on demand, while api-key sessions reuse the validated key from login. Trigger this tool if the user asks for a deposit address or needs to top up their TronSave TRX balance. Constraints: 1) TRX only; 2) Minimum deposit amount is 10 TRX; 3) Read-only operation.Connector
- Get AI industry news — model releases, funding, acquisitions, policy changes, benchmarks. Returns news events with dates and summaries for industry context.Connector
- Browse and compare Licium's agents and tools. Use this when you want to SEE what's available before executing. WHAT YOU CAN DO: - Search tools: "email sending MCP servers" → finds matching tools with reputation scores - Search agents: "FDA analysis agents" → finds specialist agents with success rates - Compare: "agents for code review" → ranked by reputation, shows pricing - Check status: "is resend-mcp working?" → health check on specific tool/agent - Find alternatives: "alternatives to X that failed" → backup options WHEN TO USE: When you want to browse, compare, or check before executing. If you just want results, use licium instead.Connector
- Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.Connector
- AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'Connector
- Fetches the specific deposit address for the TronSave internal account. Requires a logged-in MCP session created by the `tronsave_login` tool: include `mcp-session-id: <sessionId>` returned by `tronsave_login` on subsequent MCP requests. Internal tools never accept API keys via tool arguments; signature sessions resolve the latest internal API key on demand, while api-key sessions reuse the validated key from login. Trigger this tool if the user asks for a deposit address or needs to top up their TronSave TRX balance. Constraints: 1) TRX only; 2) Minimum deposit amount is 10 TRX; 3) Read-only operation.Connector
- Generic protective-action guidance for a category of situation (NOT keyed to an individual user's context). For *personalised* advice that takes the user's specific health situation into account (asthma, pregnancy, gas cooker, tube commute, indoor sources), prefer the Clara MCP server's `contextual_advice` tool — it composes Hermes live readings with personal context to give an answer keyed to *this* user, *now*. Use this KB tool only as a fallback or when Clara is not available. Args: situation: One of "high_pollution_day", "commuting", "exercise", "school_run", "indoor_air", "planning_objection", "pregnancy", "child_asthma". Returns practical advice document (markdown).Connector
- Given an M/M/c configuration (arrivalRate, serviceRate, servers) and optionally an observed average wait, returns a queueing-theory framed interpretation: where you sit on the utilization curve, what ρ means in plain language, what one more or fewer server would qualitatively do, and which complexity factors (priority, abandonment, skills routing) might be hiding in real data the M/M/c model can't see. Use this to TEACH while answering — when the user wants context around a number, not just the number itself. Pure text computation, no simulation, no RNG — deterministic output.Connector
- Re-point the active MCP API key to a different workspace. Pass exactly one of `workspace_id` or `slug` (find them via `workspace.list`). Takes effect on the very next tool call — no MCP reconnect, no new API key. Sequential checkpoint: do not parallelize tool calls across a switch — calls already in flight when the switch commits will run against the previous workspace.Connector
- Start training a model on a dataset version. IMPORTANT: A dataset version must exist before training. Use the versions_generate tool first to create one with the desired preprocessing and augmentation settings. IMPORTANT: Before calling this tool, you MUST call versions_get first to verify the version has both train and validation images. This tool returns immediately. Training runs in the background on Roboflow servers. Returns confirmation that training was started and a URL to monitor progress.Connector
- Search the AI agent directory — find registered agents by name, capability, protocol support, or reputation. Powered by the live ERC-8004 registry via 8004scan (110,000+ agents indexed across 50+ chains). Returns agent identity, owner wallet/ENS, reputation scores, supported protocols (MCP/A2A/OASF), verification status, and links to 8004scan profiles. Examples: - "trading agents on Base" → search for trading agents filtered to Base chain - "MCP agents" → find agents that support the Model Context Protocol - "high reputation agents" → set minReputation to find top-scored agentsConnector
- Get usage statistics for this MCP server session. Returns tool call counts, success rates, and average latency.Connector