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. Dates show when Glama detected each change.
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 cover the read-only, idempotent, and non-destructive nature of this tool, so the description carries less burden. It adds useful behavioral context beyond annotations: the output is clean markdown and navigation/ads are stripped. This tells the agent what to expect from the result, which is valuable and not redundant with the annotations.
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?
The description is a single sentence that front-loads the action ('Fetch any web page'), states the output format and cleaning behavior, and then gives the use case. Every clause earns its place with no wasted words.
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 simple read-only fetch tool, the description covers the essential purpose, output format, and typical use case. The schema covers the required _apiKey detail, and annotations cover the safety profile. Some minor gaps exist (e.g., behavior on non-HTML content or rate limits), but these are not critical given the tool's simplicity and the surrounding structured information.
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% for both parameters (url and _apiKey), including that _apiKey is required and how it can be obtained. The description adds no additional parameter-level detail, so it meets the baseline but does not exceed what the schema already provides.
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 a specific verb ('Fetch'), a resource ('any web page'), and the output format ('clean, LLM-ready markdown'). It also mentions stripping nav/ads, which distinguishes it from search and other URL-related tools in the sibling list without ambiguity.
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 this tool: when the agent needs the content of a specific URL rather than a search or comparative analysis. It doesn't explicitly name alternatives or exclusions, but the use case is clear enough for selection.
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.
Most tools have clearly distinct roles, but there is notable overlap in the ask_pipeworx family (stable, beta, grounded, deep_research), and ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx. discover_tools and suggest_questions also both serve as meta-guidance, creating some selection ambiguity. The very detailed descriptions help, but they do not fully remove the risk of misselection.
The set mixes verb-first names like compare_entities and validate_claim with noun-first names like entity_profile and polymarket_edges, along with prefix families (polymarket_*, pipeworx_*, ask_pipeworx_*) and bare verbs like forget and search. Everything is snake_case and readable, but there is no single predictable convention across the whole surface.
33 tools for a server labeled 'Jina Reader' is a sprawling surface spanning data routing, prediction markets, memory, subscriptions, and web reading. Even if each tool is individually focused, the count and combined scope feel oversized relative to the server name and the rubric's guidance for well-scoped servers.
Each sub-domain has good lifecycle coverage: memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list_subscriptions/recent_alerts), entity research (resolve_entity/entity_profile/compare_entities/recent_changes/validate_claim), and Polymarket analysis (edges/arbitrage/fill_risk/kalshi_spread). There are minor gaps like no order execution or simple page summarization, but those appear intentionally out of scope, leaving the surface largely complete.