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457,785 tools. Updated 2026-08-14 15:50

"An MCP server for finding exact image matches" matching MCP tools:

  • Attach an image to an existing product by giving Partle a public URL to download the image from. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. **When to use this tool**: the image is already hosted at a public URL (a scraped product page, an Imgur link, a CDN URL the user provided). Partle's server fetches it and stores it. **When NOT to use this tool**: you have local image bytes (a file the user attached, or bytes you generated/downloaded in your sandbox). Sending those bytes through a tool argument blows past conversation context limits — phone-photo-sized payloads can be 6+ MB of base64. Instead, in your code-execution sandbox, POST the file directly to the HTTP endpoint with multipart encoding: requests.post( "https://partle.rubenayla.xyz/v1/external/products/{product_id}/images", files={"file": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Or, to create the listing and attach an image in one HTTP request: requests.post( "https://partle.rubenayla.xyz/v1/external/products", data={"metadata": json.dumps({"name": ..., "price": ...})}, files={"image": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Args: product_id: ID of the product to attach the image to. image_url: Publicly fetchable URL of the image. Server fetches it and stores it. api_key: Optional API key (`pk_*`, generate at /account). Used when there is no OAuth token, and also when the OAuth token lacks the required scope — an explicitly passed key overrides an ambient token that is scoped too narrowly. An invalid or revoked token still fails regardless. Omit when using OAuth. Returns: The created `ProductImage` record with its `id` (use for deletion) and storage path, or ``{"error": ...}`` on validation/auth failure.
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  • Full-text keyword search across all archive colour names and notes. Find colours by name fragment, material, cultural reference, pigment type, or historical period. Complements conceptual embedding search with exact keyword matching. Examples: 'cerulean' (28 matches, e.g. Bourton Cerulean), 'Prussian' (187 matches spanning pigment history), 'medieval' (over 1,000 matches across period archives). Never returns a bare empty result for a genuinely obscure query -- result_path in the response is 'direct' (exact keyword hit), 'broadened' (archive restriction dropped), or 'redirected' (fell back to conceptual/semantic search) so you always know which one fired. Set entity_mode='exact' to search by botanical identity rather than by word: a plain query for 'Rose' matches any cultivar name containing it (including Sweet Peas called 'Rose Pink'), whereas entity_mode='exact' returns genus Rosa only and discloses how many off-genus records were excluded.
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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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  • Attach an image to an existing product by giving Partle a public URL to download the image from. Authenticated. OAuth (scope `products:write`) preferred; `api_key` fallback. **When to use this tool**: the image is already hosted at a public URL (a scraped product page, an Imgur link, a CDN URL the user provided). Partle's server fetches it and stores it. **When NOT to use this tool**: you have local image bytes (a file the user attached, or bytes you generated/downloaded in your sandbox). Sending those bytes through a tool argument blows past conversation context limits — phone-photo-sized payloads can be 6+ MB of base64. Instead, in your code-execution sandbox, POST the file directly to the HTTP endpoint with multipart encoding: requests.post( "https://partle.rubenayla.xyz/v1/external/products/{product_id}/images", files={"file": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Or, to create the listing and attach an image in one HTTP request: requests.post( "https://partle.rubenayla.xyz/v1/external/products", data={"metadata": json.dumps({"name": ..., "price": ...})}, files={"image": open("/path/to/photo.jpg", "rb")}, headers={"X-API-Key": "pk_..."}, ) Args: product_id: ID of the product to attach the image to. image_url: Publicly fetchable URL of the image. Server fetches it and stores it. api_key: Optional API key (`pk_*`, generate at /account). Used when there is no OAuth token, and also when the OAuth token lacks the required scope — an explicitly passed key overrides an ambient token that is scoped too narrowly. An invalid or revoked token still fails regardless. Omit when using OAuth. Returns: The created `ProductImage` record with its `id` (use for deletion) and storage path, or ``{"error": ...}`` on validation/auth failure.
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  • Generate a CDN-cached image variant for a file stored in UploadKit Cloud. Requires a paid plan, a live API key in the MCP process environment as UPLOADKIT_API_KEY, and an image key returned by UploadKit. BYOS files are not supported. Use signed delivery for private or temporary content and public delivery for stable URLs in websites, apps, srcset, CSS, or stored application data. Explicit formats consume 1 transformation unit; auto consumes 3 units. When to use: after an image is uploaded and the user wants a resized, cropped, optimized, or converted delivery URL. The returned URL is safe to send to browsers; the API key remains server-side. Returns: JSON { url, expiresAt, delivery, transform, usage }. Has the side effect of reserving monthly transformation units for a new unique variant.
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  • Connectivity check — returns server version and current timestamp. Use to verify MCP server is reachable before calling other tools.
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Matching MCP Servers

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  • 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.).
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  • 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."
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  • Full-text keyword search across all archive colour names and notes. Find colours by name fragment, material, cultural reference, pigment type, or historical period. Complements conceptual embedding search with exact keyword matching. Examples: 'cerulean' (28 matches, e.g. Bourton Cerulean), 'Prussian' (187 matches spanning pigment history), 'medieval' (over 1,000 matches across period archives). Never returns a bare empty result for a genuinely obscure query -- result_path in the response is 'direct' (exact keyword hit), 'broadened' (archive restriction dropped), or 'redirected' (fell back to conceptual/semantic search) so you always know which one fired. Set entity_mode='exact' to search by botanical identity rather than by word: a plain query for 'Rose' matches any cultivar name containing it (including Sweet Peas called 'Rose Pink'), whereas entity_mode='exact' returns genus Rosa only and discloses how many off-genus records were excluded.
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  • Full-text keyword search across all archive colour names and notes. Find colours by name fragment, material, cultural reference, pigment type, or historical period. Complements conceptual embedding search with exact keyword matching. Examples: 'cerulean' (28 matches, e.g. Bourton Cerulean), 'Prussian' (187 matches spanning pigment history), 'medieval' (over 1,000 matches across period archives). Never returns a bare empty result for a genuinely obscure query -- result_path in the response is 'direct' (exact keyword hit), 'broadened' (archive restriction dropped), or 'redirected' (fell back to conceptual/semantic search) so you always know which one fired. Set entity_mode='exact' to search by botanical identity rather than by word: a plain query for 'Rose' matches any cultivar name containing it (including Sweet Peas called 'Rose Pink'), whereas entity_mode='exact' returns genus Rosa only and discloses how many off-genus records were excluded.
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  • Reference-first build — 'show me what you like, I'll build it.' Give a reference you LIKE (a public site URL via reference_url, and/or a curated library id via reference_id from search_references) plus what YOU are building, and get back a matching StandOut direction, a reference-backed build plan, and the next call for section code. This is the front door for building from an EXAMPLE instead of a text brief: Standout reads the URL server-side for its semantic signal (title, headings, copy) and folds it into the direction match. Note: pixel-level palette/type extraction from an image is not yet supported (that is the Phase 2 vision pass) — for now it matches on the page's text/structure plus the curated library. Pass reference_id to anchor on an exact curated reference.
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  • Attach a FILE (image/blob) to an issue over MCP. Returns { uploadUrl } for the issue; then POST the raw file bytes to it with your API key as a Bearer header — the server encrypts the file at rest and attaches it. Add `&filename=` (download name) and optional `&title=` to the URL. Example: `curl -X POST "<uploadUrl>&filename=shot.png" -H "Authorization: Bearer <yourApiKey>" --data-binary @shot.png` → {"id":"...","url":"..."}. For a plain link (not a file), use save_attachment instead.
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  • Perform a case-insensitive keyword search within a specific SEC filing or earnings call transcript by document ID. Returns matching lines with surrounding context and line numbers, making it ideal for finding exact terms, figures, or phrases that semantic search might miss. Typographic punctuation is folded before matching, so a plain-ASCII keyword (e.g. "world's") matches the smart punctuation stored in filings. The header reports the total number of matching lines even when only the first ones are shown. Use this after ListCompanyDocuments to locate precise occurrences of a keyword (e.g., a revenue figure, risk factor term, or executive name) within a known document. Complements semantic search tools by providing exact text matches rather than meaning-based results. Use ReadDocumentLines to read broader sections around matches.
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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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  • 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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  • 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.
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  • Estimate the USD cost of an MCP tool call BEFORE invoking it. Returns median + P90 + average cost from the org's last-30-day history for this exact (server, tool) combo. Use this to make spend-aware decisions in your agent — e.g. confirm with the user before invoking tools where the estimate exceeds your budget. Returns isUnknown=true with zero cost when no baseline exists yet.
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