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Read Url

read_url
Read-onlyIdempotent

Fetch any web page as clean, LLM-ready markdown (strips nav/ads) — ideal for giving an agent the readable content of a URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe full target URL to fetch, e.g. "https://example.com/article".
_apiKeyNoYour 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.

TDQS

A4.2/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and specialized tools like entity_profile or validate_claim that can answer similar questions. This creates ambiguity for an agent trying to select the correct tool.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, with most using a verb_noun structure (e.g., ask_pipeworx, compare_entities, resolve_entity). There are no mixed conventions or chaotic naming.

Tool Count3/5

With 31 tools, the server is on the heavy side. While each tool has a distinct purpose, the number is borderline for a coherent set and could be streamlined, especially given the overlapping functionality.

Completeness3/5

The tool set covers a wide range of query and analysis tasks, including data lookup, comparison, betting research, memory, and subscriptions. However, there are notable gaps (e.g., no update/delete for most data, no user management) and some tools seem out of place (e.g., generate_llms_txt).