Skip to main content
Glama
458,064 tools. Updated 2026-08-14 22:15

"A GitHub repository for an MCP server with a clear description of config.json" matching MCP tools:

  • Pro/Teams — return the authenticated user's validation run history for all three lenses (architect.validate → validator='architecture', design.validate → validator='surface', spec.validate → validator='spec') with the Blueprint Readiness Score (0-100), letter grade (A-F), and tier (draft, emerging, production_ready). Each run carries a `validator` field naming its lens. Three lookup modes: (1) `run_id=<id>` returns a SINGLE run with the full persisted result_json — use this to RECOVER a result when your MCP client tool-call timed out before architect.validate, design.validate, or spec.validate returned. The run completes server-side and persists; the run_id is surfaced in the first progress notification of every validate call so you have the recovery handle even when your client gives up early. (2) `repository=<name>` returns the full per-run trend for that repository plus a regression diff between the latest two runs. (3) No arguments returns one summary per repository the user has validated, sorted by most recent. Use modes (2) or (3) BEFORE re-validating the same repository on either lens — they tell you which principles or laws regressed since the last run, so you can focus the new review on what is actually changing. Auth: Bearer <token>. Pro or Teams plan required.
    Connector
  • 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.
    Connector
  • Scan a public GitHub MCP-server repository for security issues. Clones the repo (shallow, <60s, <200 MB), runs compuute-scan v0.6.2 in static analysis mode (no code execution from the target), and returns a structured report with severity counts, a 0-100 score, and the 10 most severe findings. WHEN TO USE: - Before connecting to an unknown MCP server discovered via Anthropic Registry, Smithery, mcp.so, or a Discord recommendation. - Before installing a third-party MCP-server package into a production pipeline. - As part of an agent's pre-commit / pre-deploy due-diligence step when adding new dependencies. - As one input to a multi-source trust evaluation (combine with publisher reputation, package install count, last-update recency). WHEN NOT TO USE: - For private repos. Use the on-prem CLI instead: `npx compuute-scan ./path-to-private-repo` - For deep exploitability assessment of a specific code path. This is pattern matching, not dataflow analysis. Book a manual L2-L4 audit at https://compuute.se/audit for that depth. - For non-GitHub hosts (GitLab, Bitbucket, self-hosted). v1 supports github.com only. - For repos > 200 MB or clone time > 60s. The endpoint returns a 413 or 504 in those cases — fall back to local CLI. EXPECTED RESPONSE TIME: - Median: ~1-2 seconds for small repos (<100 files). - p99: ~10 seconds for medium repos. - Hard timeout at clone=60s, scan=120s combined. EXPECTED COST: - Free tier in MVP. Future Pro tier may charge per-scan or per-month. DATA FRESHNESS: - Scanner version is reported in response.scanner.version. - L1 rule set freshness reflects compuute-scan releases — see github.com/Compuute/compuute-scan/CHANGELOG.md for the latest CVE and threat-intel response timeline. EXAMPLES: Example 1 — scan an MCP server you're evaluating: github_url = "https://github.com/modelcontextprotocol/servers" → score: 0, summary: {critical: 1, high: 94, medium: 22} → top_findings include SSRF, eval, etc. → recommendation: "AVOID — 1 critical and 94 high finding(s)..." Example 2 — scan a clean reference implementation: github_url = "https://github.com/microsoft/azure-devops-mcp" → score: 90+, summary: {critical: 0, high: 1} → recommendation: "REVIEW — 1 high finding(s)..." Example 3 — scan your own dev MCP-server before publishing: github_url = "https://github.com/yourorg/your-mcp" → audit your own surface before others install it OUTPUT FIELDS (stable schema): - repo_url (str): canonical URL of the scanned repo. - score (int): 0-100, higher safer. Coarse summary, not a precision claim. - summary (object): {critical, high, medium, low, info, files_scanned}. - recommendation (str): action guidance derived from severity counts. - findings_count (int): total raw findings (may include false positives). - top_findings (list): up to 10 most severe, each with {id, title, severity, file, line, owasp, cwe}. - l0_discovery (object): MCP transport, tool count, dependency pinning. - performance (object): clone_seconds, scan_seconds, repo_size_bytes. - scanner (object): {name, version, layers_covered}. - _disclaimer (str): MANDATORY triage disclaimer. Read it. Args: github_url: Public GitHub HTTPS URL (e.g. https://github.com/org/repo). Must be public and < 200 MB. v1 is github.com only. Returns: Structured scan result. On error, returns {"error": code, "message": ...} with HTTP-style code (invalid_url, clone_failed, scan_timeout, etc.).
    Connector
  • 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."
    Connector
  • Read-only: returns what Apex by LeadShark is, tier pricing, and the URL of the real authenticated MCP server. Call this first — this endpoint is a discovery stub with no LinkedIn powers.
    Connector
  • Get SaSame-observed MCP server recommendations for a capability you need. SaSame is one modular MCP Factory with permanent independent observation and evidence stations; 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.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Search GitHub repos, issues, pull requests, and user profiles for development intelligence via MCP.

  • Manage your Canvas coursework with quick access to courses, assignments, and grades. Track upcomin…

  • Use for a quick latest-version package adoption or health check: license, description, repository health, downloads, publish age, and latest vulnerability status. Latest-version package overview for dependency triage. Provide `registry` and `package_name` (for example `npm` + `express`). Default text returns license, description, repository popularity (stars/forks/issues and [ARCHIVED] when applicable), downloads, publish age, and vulnerability status. Set `verbose: true` for GitHub language/topics/last-pushed, recent advisories, and recent changes. Pass `format: "json"` for structured fields. Use `pkg_vulns` for version-specific vulnerability details.
    Connector
  • Analyze an MCP tool definition for instruction-injection and malicious patterns. Performs semantic fingerprinting of the tool's description, parameter schemas, and error templates — detecting credential exfiltration vectors, C2 callbacks, base64 payloads, authority spoofing, and injection phrase patterns. Also checks the tool hash against the SKILL IOC feed and the description against the PROMPT IOC feed for known-malicious matches. If track=True (default), the tool definition is compared against a stored baseline and semantic drift is detected on subsequent calls for the same tool. Args: tool_def: MCP tool definition dict. Expected keys: name, description, inputSchema (optional), annotations (optional). registry: Registry this tool came from ("mcp.so", "clawhub", "smithery", "npm", "pypi", "github", or "unknown"). track: If True, maintain baseline and detect drift across calls. Returns: tool_name: Tool name tool_hash: SHA256 of canonical tool definition risk: "clean" | "low" | "suspicious" | "malicious" risk_score: 0.0–1.0 should_block: True if risk == malicious should_warn: True if risk >= suspicious signals: List of detected signals with field, pattern, excerpt prompt_ioc_matched: True if description matched PROMPT IOC feed skill_ioc_matched: True if tool hash matched SKILL IOC feed latency_ms: Analysis latency drift: Drift result (if track=True and tool was seen before)
    Connector
  • 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/
    Connector
  • 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.
    Connector
  • 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.
    Connector
  • DEV ONLY — Sign and broadcast an unsigned transaction using a local private key (PK env var). For production, use a dedicated wallet MCP server (Fireblocks, Safe, Turnkey, etc.) instead of this tool. Takes the transaction object returned by any write.* tool and submits it onchain.
    Connector
  • Pre-flight security verdict for an MCP server invocation. Judges BOTH server-level reputation AND the server's dependency graph (npm/pypi) against the DugganUSA threat-intel corpus (1.13M+ IOCs, Shai-Hulud + typosquat + LOLBin families). Returns BLOCK / ADVISORY / REVIEW / ALLOW with severity, evidence, dep-graph summary, and HMAC-signed response. REVIEW means we hold NO RECORD of this server -- not that it is safe. Treat REVIEW as do-not-proceed-blindly: a brand-new attacker-published server looks exactly like this. ALLOW is only returned when we actually resolved the server and scanned its dependency graph; check known_to_us and dep_graph.scanned to confirm. Use this BEFORE invoking any other MCP server tool, especially ones installed from outside the official MCP Registry.
    Connector
  • FREE preview scan of a target MCP server for tool-poisoning / prompt-injection. Returns issue count, severity breakdown, risk score, and verdict (clear/review/block) — but NOT which tools or the evidence. Use this to check any MCP server (including your own) at no cost; if issues are found, call the paid scan_mcp_server for the itemized findings + remediation. No payment required.
    Connector
  • 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.
    Connector
  • Fetch the complete record for ONE MCP server in the agentage directory by its canonical slug: full description, categories, the packages and remote endpoints it ships, the tools it exposes, a ready-to-run install command, and a README excerpt. Use this after mcp_search to get the depth a result card omits - pass a slug exactly as returned by mcp_search. Slugs are canonical and registry-derived ("io-github-github-github-mcp-server"), NOT the plain product name ("github"); if you pass a plain name anyway it is resolved by search as a fallback - a single confident match returns that server (with `resolved_from` set), anything else returns an error naming the candidate slugs to retry with. No slug yet? call mcp_search first. Read-only.
    Connector
  • 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.
    Connector
  • Apply a named motion preset (glow_pulse, ken_burns, scale_pop, ...) to a visual layer in an MP Scene. Returns the updated scene with the preset's animations + effects merged into the target layer. Each preset declares an `appliesTo` set of layer content kinds (text/media/color) -- mismatches are rejected with a clear `preset_kind_mismatch` error. Discoverable presets and their parameter shapes are listed under `supports.motionPresets` in `picsart_media_get_capabilities`. Pure: takes the full scene by value, returns a new scene; no server-side state.
    Connector