Generate a briefing from multiple URLs or topics. Supports executive, bullet, and detailed formats. Pay per call (0.015 USDC) or use subscription.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | No | Array of URLs to include in the brief | |
| focus | No | Optional focus area for the briefing | |
| format | No | Briefing format style | executive |
| topics | No | Array of topics to search and include in the brief |
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?
With no annotations, the description bears full burden. It discloses the tool is paid but omits other behavioral traits like rate limits, authentication requirements, or output characteristics (e.g., length, structure). No mention of side effects.
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?
Two sentences, 14 words, front-loaded with the core function. No fluff, but the brevity sacrifices behavioral and usage details. Could include a bit more without being verbose.
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 no output schema and no annotations, the description is minimally adequate. It explains the main purpose and parameters but lacks details on return format, error handling, or integration with other tools. Leaves gaps for a 4-parameter tool.
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%, baseline 3. The description adds value by clarifying that the tool accepts 'multiple URLs or topics', reinforcing the flexibility of the 'urls' and 'topics' parameters, and listing format styles that map to the 'format' enum.
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 generates a briefing from URLs or topics, specifying supported formats (executive, bullet, detailed). This distinguishes it from siblings like 'analyze_text' or 'extract_content' which perform different operations.
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 mentions pay-per-call and subscription options but provides no guidance on when to use this tool versus siblings (e.g., 'research_topic' vs 'daily_brief'). No explicit when-to-use or when-not-to-use criteria.
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.