URL to Markdown or structured JSON (schema.org/OG); renders SPA; batch reads. No signup/key.
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap. The tool's purpose is clearly defined and distinct.
The single tool is named 'read', a clear and simple verb. There are no other tools to create inconsistency, so the naming is inherently consistent.
A single tool feels minimal, but for a focused web-reading/parsing service it is borderline acceptable. It would be better with a couple more tools (e.g., separate extract/jobs modes), but the count is not extreme.
The tool covers a wide range of formats (HTML, text, JSON, XML, jobs) and provides clean Markdown extraction. For its stated purpose, there are no obvious missing operations.
Fetch a web page and return its main content as clean Markdown (headings, links, lists, code preserved; nav/ads/scripts stripped). Also handles plain text, JSON and XML, extracts structured data (format=data), and normalizes any ATS/job-board feed into a unified jobs[] array (format=jobs). Use this whenever you need to read the actual content of a URL. No signup, no API key.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Absolute http(s) URL to read, e.g. https://example.com/post | |
| links | No | Keep hyperlinks as [text](url). Default true. Set false for plainer prose. | |
| format | No | Output format. 'markdown' (default) = clean readable content; 'text' = plain prose, no link markup; 'data' = extract the page's embedded structured data (schema.org JSON-LD, OpenGraph, Twitter card, meta) as JSON; 'jobs' = normalize any ATS/job-board feed (Greenhouse, Lever, Ashby, Workable, SmartRecruiters, Recruitee) or a careers page with JSON-LD JobPosting into a unified jobs[] array (title, url, location, department, employment_type, updated_at, remote) — ideal for job aggregation with no per-provider parsing. | |
| render | No | JS rendering for client-side/SPA pages. 'auto' (default) renders with a headless browser only when the server HTML looks empty; 'js' forces a full browser render; 'off' never renders. The response 'rendered' field says whether a browser was used. | |
| max_chars | No | Optional cap on returned characters (content is truncated if longer). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that nav/ads/scripts are stripped, that JS rendering can occur ('rendered' field indicates), and that content is truncated with max_chars. This gives the agent a clear picture of the tool's behavior and limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Although the description is long, it is front-loaded with the core function, then systematically covers formats and parameters. The detailed explanation of 'format=jobs' earns its place given the tool's complexity. No wasted words or repetition of schema info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given moderate complexity (5 params, no output schema), the description covers return formats, behavioral details (rendering, truncation), and special cases (ATS/job-board feeds). It explains enough for an agent to select and invoke correctly without needing additional documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description goes well beyond by explaining the 'format' parameter in detail (e.g., 'jobs' normalizes feeds from specific platforms into a unified schema, 'data' extracts structured metadata). This provides actionable meaning beyond the enum labels.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and resource ('a web page'), and clearly states the main output (clean Markdown). It also enumerates additional supported formats (text, data, jobs), making the tool's purpose unambiguous even with no sibling tools to differentiate from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Use this whenever you need to read the actual content of a URL,' giving a direct condition for use. It also notes 'No signup, no API key' making adoption easy. However, it does not mention when not to use the tool or suggest alternative approaches, such as when only metadata is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
read2 fields changedInput schema / properties / format / descriptionPrevious value: -"Output format. 'markdown' (default) = clean readable content; 'text' = plain prose, no link markup; 'data' = extract the page's embedded structured data (schema.org JSON-LD, OpenGraph, Twitter card, meta) as JSON — ideal for price/spec/job/product aggregation with no scraping."New value: +"Output format. 'markdown' (default) = clean readable content; 'text' = plain prose, no link markup; 'data' = extract the page's embedded structured data (schema.org JSON-LD, OpenGraph, Twitter card, meta) as JSON; 'jobs' = normalize any ATS/job-board feed (Greenhouse, Lever, Ashby, Workable, SmartRecruiters, Recruitee) or a careers page with JSON-LD JobPosting into a unified jobs[] array (title, url, location, department, employment_type, updated_at, remote) — ideal for job aggregation with no per-provider parsing."Input schema / properties / format / enumPrevious value: -[
- "markdown",
- "text",
- "data"
-]New value: +[
+ "markdown",
+ "text",
+ "data",
+ "jobs"
+]read1 field changedInput schema / properties / formatAdded value: +{
+ "description": "Output format. 'markdown' (default) = clean readable content; 'text' = plain prose, no link markup; 'data' = extract the page's embedded structured data (schema.org JSON-LD, OpenGraph, Twitter card, meta) as JSON — ideal for price/spec/job/product aggregation with no scraping.",
+ "enum": [
+ "markdown",
+ "text",
+ "data"
+ ],
+ "type": "string"
+}read1 field changedInput schema / properties / renderAdded value: +{
+ "description": "JS rendering for client-side/SPA pages. 'auto' (default) renders with a headless browser only when the server HTML looks empty; 'js' forces a full browser render; 'off' never renders. The response 'rendered' field says whether a browser was used.",
+ "enum": [
+ "auto",
+ "js",
+ "off"
+ ],
+ "type": "string"
+}readLink-preview metadata and clean page-to-Markdown for any public URL. No install.
Turn any URL into clean Markdown and structured data. Scrape, crawl, search and extract.
Turns any URL into SEO metadata, contacts, tech stack, and AI-ready Markdown, in one call.
Fetch a URL and get clean Markdown with metadata. No API key required; rate-limited per IP.
Add one secure layer between your agents and this server.
