Analyze sentiment trends across multiple sources (URLs or texts). Compares and synthesizes sentiment. Pay per call (0.008 USDC) or use subscription.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | No | Array of URLs to analyze for sentiment | |
| texts | No | Array of text contents to analyze for sentiment | |
| topic | No | Optional topic context for analysis |
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 provided, the description carries the full burden. It discloses pay-per-call cost (0.008 USDC) and subscription option, which is a useful behavioral trait. However, it does not mention non-destructiveness, rate limits, error handling, or what happens to the data, leaving significant gaps.
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 very short (two sentences) and conveys the core purpose efficiently. However, it could be slightly more front-loaded by leading with the primary action and cost, rather than repeating 'sentiment' themes. Still, it earns its place with minimal waste.
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 optional parameters, no output schema, and no annotations. The description fails to explain what the tool returns (e.g., a trend chart, scores, or summary). It also omits the 'over time' temporal aspect implicit in the name. For a tool with this complexity, the description is insufficient for an agent to fully understand its behavior.
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%, so the baseline is 3. The tool description adds no additional parameter guidance beyond what the schema already provides. It does not explain how parameters like 'topic' affect analysis or provide usage examples.
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 analyzes sentiment trends across multiple sources (URLs or texts), and compares/synthesizes sentiment. This distinguishes it from sibling tools like analyze_text (single text analysis) and compare_articles (comparison of articles). However, the 'over time' aspect from the name is not explicitly mentioned, which would improve clarity.
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?
No explicit guidance on when to use this tool versus alternatives. It mentions pay-per-call cost but does not discuss when not to use it or how it differs from sibling tools like analyze_text or competitor_intel. The context is implied but not directly stated.
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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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.