Compare two sources (URLs or text) for similarities, differences, coverage, and bias. Pay per call (0.01 USDC) or use subscription.
| Name | Required | Description | Default |
|---|---|---|---|
| aspect | No | Optional focus aspect (e.g., 'security', 'performance') | |
| source_a | Yes | First source URL or text content | |
| source_b | Yes | Second source URL or text content |
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 must disclose behavioral traits. It mentions the core behavior (compare for similarities, etc.) and the cost model (0.01 USDC per call or subscription). However, it omits side effects, authorization needs, rate limits, or what happens on invalid input. The cost disclosure adds some transparency but not enough.
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, each serving a distinct purpose: first describes core functionality, second states pricing. No wasted words, well-structured and front-loaded with the primary purpose.
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?
The tool has three parameters with full schema coverage but no output schema. The description does not explain return values, error conditions, or behavior for edge cases (e.g., invalid URLs). For a simple tool, it covers the basics but lacks completeness for agent usage.
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 baseline is 3. The description adds clarifying context that sources can be URLs or text (already implied by schema descriptions) and mentions an optional aspect focus. This provides marginal added value beyond the schema.
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 it compares two sources (URLs or text) for similarities, differences, coverage, and bias. It uses a specific verb ('compare') and identifies the resource (sources), distinguishing it from siblings like analyze_text (single text analysis) or competitor_intel (competitive analysis).
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 no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusion criteria. The only additional instruction is pricing (pay per call or subscription), which does not help the agent decide when to invoke the tool.
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.