url-context-mcp (SkillfulElectro/url-context-mcp) is an MCP server listed on the M8ven Trust Index. It scores 74 out of 100, grade C. It declares 5 tools. No publisher has claimed this listing.

C
Emerging
74/100

url-context-mcp

An MCP server that fetches web pages and extracts clean, AI-usable context from them, enabling tools for link discovery, content search, and integrated fetch-and-search operations.

Emerging. No concerning findings. Grades remain capped until the project builds reputation through adoption. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

How we verified

Code Verified⚡ Live Monitored: not connected

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Who stands behind it

SkillfulElectro

Source: Glama

Is this your MCP?

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Install from

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// tools this server exposes5 tools

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

fetch_url
extract_links

Extract all hyperlinks from a URL (or raw HTML). Returns array of {href, text, title, section, isExternal, hash, type} grouped to dedupe. Great for discovering pages/anchors before fetching them.

search_content

Search the content of a URL (or raw HTML) and return matching snippet(s) with surrounding context and line numbers. Useful to find specific text/code/errors on a page without reading the whole thing.

fetch_and_search

One-shot combo: fetch a URL and simultaneously return BOTH the cleaned content AND its links AND any matching search snippets. Most efficient when you want to understand a page and also navigate/search it. Omit pattern/links as needed.

fetch_image
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

5/5 tools missing one or more hints — fetch_url (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); extract_links (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); search_content (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +2 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Tests exist

No test files found

Add tests that exercise each declared tool.

Shell command execution

1 child_process call — runs shell commands

Prefer library functions over shell-outs. If you must shell out, ensure all inputs are properly escaped.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 4 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
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commit: 47532e82427cb7a984566812813ed87a207be2c6
code hash: e6611490fa96994ce8eeddf018a9410b108bf5cb004403862048c1fe4ba2d306
view raw JSON →
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