read_urlFetch any web page as clean, LLM-ready markdown (strips nav/ads) — ideal for giving an agent the readable content of a URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The full target URL to fetch, e.g. "https://example.com/article". | |
| _apiKey | No | Your Jina API key. Required — Jina no longer serves anonymous requests. Pipeworx injects its own when one is configured; free keys at https://jina.ai/reader. |
Changes observed during successful MCP inspections.
Input schema / examplesPrevious value: -[
- {
- "url": "https://example.com/article"
- },
- {
- "_apiKey": "your-jina-reader-api-key",
- "url": "https://news.example.com/story"
- }
-]New value: +[
+ {
+ "url": "https://example.com/article"
+ },
+ {
+ "url": "https://news.example.com/story"
+ }
+]Input schema / properties / _apiKey / descriptionPrevious value: -"Optional — your own Jina API key for higher rate limits; works without one."New value: +"Your Jina API key. Required — Jina no longer serves anonymous requests. Pipeworx injects its own when one is configured; free keys at https://jina.ai/reader."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful behavioral detail beyond that: it returns markdown and strips navigation/ads, which helps an agent understand what kind of output to expect.
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?
A single sentence conveys the tool's purpose, output format, and typical use case with no redundant wording. The key information is front-loaded and the sentence earns its place.
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?
For a low-complexity URL-fetching tool, the description is complete: the schema covers all parameters, annotations cover safety, and the description states the output format. No output schema exists, but the markdown behavior is explicitly disclosed.
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 description coverage is 100%, so the url and _apiKey parameters are fully documented in the input schema itself. The description adds no parameter-level detail, so the baseline score of 3 is appropriate.
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 clearly states the action ('Fetch'), the resource ('any web page'), and the outcome ('clean, LLM-ready markdown'). It does not explicitly differentiate from a sibling tool, but the verb and resource are specific enough that the tool's role is unmistakable.
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?
The phrase 'ideal for giving an agent the readable content of a URL' provides clear context for when to use the tool. It does not explicitly mention when not to use it or name alternatives, but the use case is well stated for a simple fetch utility.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.