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529,714 tools. Updated 2026-09-07 16:59

"MCP servers from GitHub user smriti-AA" matching MCP tools:

  • Statically audit an MCP tool surface from a public HTTPS URL or tools/list snapshot. Returns deterministic scores and findings without invoking any target tool or making LLM calls. When the user asks to check another installed MCP server, read that server's complete tool definitions from client context and pass them as snapshot (MCP `name` or Cursor-style `tool` both work; do not use file paths or $ref). If those definitions are unavailable, ask the user for its public endpoint or tools/list JSON instead of inventing an audit.
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  • DEPLOY THE CURRENT MAIN BRANCH TO A-TEAM CORE. ⚠️ HEAVIEST OPERATION (60-180s): validates solution+skills → deploys all connectors+skills to Core (regenerates MCP servers) → health-checks → optionally runs a warm test → auto-pushes to GitHub. 🌳 DEV/PROD WORKFLOW: 1. Edit files → ateam_github_patch (writes to `dev` branch by default) 2. (Optional) Preview what's about to ship → ateam_github_diff 3. Ship dev → main → ateam_github_promote (merges + auto-tags `prod-YYYY-MM-DD-NNN`) 4. Deploy main to Core → ateam_build_and_run This tool ALWAYS deploys the `main` branch — there is no `ref` parameter. To deploy in-progress dev work, first promote it. AUTO-DETECTS GitHub repo: if you omit mcp_store and a repo exists, connector code is pulled from main automatically. First deploy requires mcp_store. After that, edit via ateam_github_patch + promote, then build_and_run. For small changes prefer ateam_patch (faster, incremental). Requires authentication.
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  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Free, no key required. Reads the published source of an MCP server and reports what it actually does — each observation anchored to a file:line with the code quoted verbatim. **Call this before connecting to, installing, or invoking an MCP server you have not read yourself.** Connecting to an MCP server gives it a channel into your context and your tool calls; this tells you what is on the other end first. Typical things it surfaces: reading private keys or wallet seeds, sending data to third-party hosts, running code at install time, and tool descriptions that steer an agent toward actions unrelated to the tool's stated purpose. Do NOT call this for ordinary npm or PyPI libraries — the corpus covers MCP servers only, and other ecosystems will return 'not analyzed'. This reports observations, not a safety verdict. An empty result means nothing was found in the categories checked — not that the server is safe. Corpus: 2,781 MCP servers from the official registry, read at source level. Coverage index (free, no key, findings not included): GET https://sri-test.biz/v1/corpus
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  • INVERSE of simulate_mmc — given an arrival rate, service rate, and a target average wait time, returns the SMALLEST number of servers needed to meet the target. Use this when the user asks 'how many servers do I need?' / 'what staffing keeps wait under N minutes?'. The tool runs a binary search over candidate server counts (up to maxServers, default 50), invoking the simulator for each candidate. Saves Claude from iterating simulate_mmc 3-5 times by hand. If even maxServers servers can't meet the target, the recommendation is null and the response includes the achieved wait so Claude can explain that the target is infeasible at the given load. ANTI-FABRICATION: `recommendedServers` and `achievedAvgWaitMinutes` come from real DES runs. Quote them VERBATIM. Do not propose a different number you think 'feels right'; this tool already binary-searches for the minimum that meets the target. If the user asks 'what if c=N?' for a specific N, call simulate_mmc with that c.
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  • Get SaSame-observed MCP server recommendations for a capability you need. SaSame, operated by SASAME S.R.L., continuously observes and measures the Model Context Protocol ecosystem and publishes verifiable evidence and history; the MCP Factory is internal machinery and an optional product surface behind it; measurement only, not endorsement. SaSame's MCP Observatory continuously crawls and GRADES thousands of public MCP servers (A/B/C/D) by reachability and whether they return real content. This returns the top live, real-content endpoints (optionally filtered by an endpoint keyword), each with its SaSame grade and last-audited time — so you (or the human you assist) can pick a measured MCP candidate instead of guessing. Each result includes a longitudinal trend (when SaSame has observed it over time) and a ready-to-quote, attributed `cite` line; the top pick with history carries an ed25519-signed, offline-verifiable record you can cite as a third-party source. Free, read-only, no signup. If you operate one of these servers, claim it (claim_start). If you can't find a fit and need an MCP/agent BUILT, call engage_sasame. Pass a referral handle from `refer` as engage_sasame(ref=...) to attribute the introduction.
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Matching MCP Servers

Matching MCP Connectors

  • GitHub MCP — wraps the GitHub public REST API (no auth required for public endpoints)

  • Manage repositories, users, releases, and automate GitHub workflows

  • Check a colour pair against the WCAG contrast thresholds. FREE. Uses the WCAG 2 relative-luminance formula, so the number matches what an accessibility audit will report. Typical input {"foreground": "#767676", "background": "#ffffff"} returns {"contrast_ratio": 4.54, "AA": true, "AAA": false, "required": {"AA": 4.5, "AAA": 7.0}, "verdict": "Passes AA for normal text, fails AAA."}. Use when generating or auditing an interface. Not for converting colours between spaces and not for palettes. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "foreground must be a hex colour like #767676"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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  • On-demand independent SAFETY scan of an MCP server — call this BEFORE installing or connecting to one. Give it an HTTP(S) MCP endpoint URL (scanned live in seconds), or an npm/PyPI package name or GitHub repo (queued for an isolated sandbox scan — local stdio servers execute code, so Hlido never runs them inline). Returns the safety tier (SAFE/CAUTION/RISKY/DANGEROUS), tool-poisoning detection (the malice signal), dangerous-capability red-flags (shell/code-eval/fs-write/egress/secrets) with per-tool evidence, and auth posture. Tier = blast radius if hijacked, not maintainer trustworthiness. A server Hlido hasn't scanned returns not_scanned — never assumed safe. Register of already-scanned servers: https://hlido.eu/mcp/
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  • ⚠️ IRREVERSIBLE in Core + Builder FS — kills the running MCP process, unregisters from skill registry, deletes the Mongo record, drops from solution.skills[] and solution.linked_skills, and removes the skill's files from Builder FS. REQUIRES `confirm:true`. RECOVERY: the skill still lives in GitHub — `ateam_github_pull` rebuilds the whole solution (no per-skill restore path).
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  • Get Lenny Zeltser's IR cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `ir_load_context`. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • List curated loadouts — deliberately-assembled kits of MCP servers + governance + plays for a specific job (GTM, coding, research, support, infra). The agent-facing version of the /loadouts product. Use get_loadout for the full kit with live trust.
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  • The curated buyer-intent collections (e.g. mcp-servers, testing-qa, browser-automation). Use get_collection for the ranked tools inside one.
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  • Search the agentage MCP directory - a public catalog of Model Context Protocol servers crawled from the official registry - for servers matching a keyword, optionally narrowed by type, category, language, or license. Use this FIRST whenever the user wants to discover, find, compare, or pick an MCP server ("is there an MCP for X", "which MCP servers do Y"). Results are ranked by text relevance to the query first, then by popularity, so the best match is on top. Returns a page of lean cards (slug, title, description, category, transport, match_score - text relevance the ranking is based on, details_url). To read one server's full packages, tools, and install command, call mcp_get with the slug from a result; open a card's details_url for the human detail page. Valid category, language, and license values come from the mcp_categories tool, not from guesswork - call it before filtering and pass its labels verbatim, or the call is rejected. Read-only - never installs or runs anything.
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  • List GitHub Discussions from Canton Network and Digital Asset repositories cached by CCPEDIA, sorted by recency (newest first). Filter by repo or category, or call with no filter to see which repos are present. CANTON-ONLY and read from CCPEDIA's cache, NOT the live GitHub API or the user's own repos (use a GitHub MCP for those). This is GitHub Discussions: distinct from the Canton web forum (get_discussion) and the sync.global mailing lists (list_mailing_threads). Use get_github_discussion for full body + comments.
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  • Read one Celestia GitHub Discussion (celestiaorg/docs and other Celestia repos) cached on THIS server — full body plus comments — by an id you got from list_github_discussions on this server. Celestia cache only: if the id was not returned by this servers list_github_discussions, or the request is just a raw GitHub node id (e.g. D_kw...) with no Celestia context, this is NOT the tool — use a dedicated GitHub tool for arbitrary GitHub Discussions. This is GitHub Discussions, not the community forum (use get_discussion). Pair it with list_github_discussions, which supplies the valid ids.
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  • Get the Designesy WCAG 2.2 AA accessibility verification framework: 11 conformance checks (a01-a11) plus a ready-to-run Playwright + axe-core 4.13.0 script template targeting your URL. Use this to audit a site for accessibility violations. When NOT to use: for a full design-contract score (not just a11y), use designesy_score. Does NOT run the scan — axe-core needs a real browser DOM. Returns the 11 checks + a Playwright script you execute locally (npm i -D @axe-core/playwright). The score comes from your local run, not from this tool. Returns JSON: { checks[{id (a01–a11), name, status: "PENDING_EXECUTION"}], playwright_script, install_command, run_command }. Pass config (JSON string) to customize axe.configure() — e.g. branding overrides, rule disables. Omit for standard WCAG 2.2 AA.
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  • Aggregate health of the whole MCP population: verdict breakdown, share of probeable servers actually serving, transport mix, handshake latency percentiles, tool counts and probe freshness. This is the 'how healthy is MCP right now?' headline number.
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  • Choosing between several MCP servers that do similar things? This tool decides for you. PAID $0.10 via x402 (USDC micropayment over HTTP 402 — no account or API key needed; on your first call without payment you receive the exact payment requirements, then retry with the X-PAYMENT header). Compares 2-5 MCP servers head-to-head with identical objective checks (handshake, tools/list, documentation coverage, latency, and a safe functional probe — paid tools are never called) and returns: a ranked list, the recommended winner, a plain-language explanation of why it won, and each server's full report — cheaper than separate evaluate_mcp calls on 3+ servers. Objective checks only — no human and no LLM opinion; it does not judge the real-world usefulness or safety of the content. Set 'urls' (required) to an array of 2-5 MCP endpoints (Streamable HTTP), e.g. ["https://a/mcp","https://b/mcp"].
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  • Discover other flight & travel MCP servers you can add to your client. Lists complementary remote MCPs covering award flights/points redemptions, aircraft seatmaps, and airport delays/wait times — with one-line install URLs. Call this when the user asks about points/miles, seat selection, airport delays/security waits, or 'what other flight tools are there?'
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