Extract structured JSON data from a URL. Define a schema or let AI infer the structure. Pay per call (0.008 USDC) or use subscription.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to extract structured data from | |
| schema | No | Optional JSON schema definition for extraction fields |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses pay-per-call pricing (0.008 USDC), which is a positive behavioral trait. However, it omits other critical behaviors such as authentication requirements, rate limits, error handling, or what happens on failure. This is insufficient for a tool with no annotation safety net.
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 two sentences, with the core action in the first sentence. It is efficient and includes key differentiators (schema option, pricing). No filler or 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?
Given the complexity (nested objects in schema, no output schema), the description is incomplete. It does not describe the return format, pagination, or data structure. Users are left guessing what the extracted JSON looks like. The presence of a sibling for content extraction suggests the need for clearer differentiation here.
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 the baseline is 3. The description adds minimal extra meaning: it implies the schema parameter is optional ('Define a schema or let AI infer'), but does not elaborate on the structure or constraints beyond what the schema descriptions provide. No significant value added.
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 tool extracts structured JSON data from a URL, specifying the action and resource. It mentions the option to define a schema or let AI infer, adding clarity. However, it does not explicitly distinguish itself from the sibling 'extract_content', leaving some 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 description provides context on when to use the tool (for structured extraction with optional schema inference), but does not give explicit guidance on when not to use it or suggest alternatives like 'extract_content'. The pricing note is useful but not usage guidance per se.
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.
Each tool has a clearly distinct purpose: general text analysis, article comparison, competitive intelligence, briefing generation, content extraction, structured data extraction, page change monitoring, research synthesis, and sentiment trend analysis. No two tools overlap in function.
Most tools follow a verb_noun snake_case pattern (e.g., analyze_text, extract_content), but 'competitor_intel' and 'daily_brief' deviate slightly (noun_noun and adjective_noun). Overall pattern is clear and predictable.
With 9 tools, the set is well-scoped for a content intelligence API. Each tool covers a key capability without being excessive or insufficient.
The tool surface covers major content intelligence tasks: analysis, comparison, extraction, monitoring, research, and sentiment. Minor gaps like keyword extraction exist, but core workflows are well covered.