Competitive intelligence analysis comparing two companies/entities. Searches web, extracts content, and provides structured comparison. Pay per call (0.025 USDC) or use subscription.
| Name | Required | Description | Default |
|---|---|---|---|
| aspect | No | Optional focus area (e.g., 'market share', 'innovation') | |
| industry | No | Optional industry context | |
| company_a | Yes | First company name | |
| company_b | Yes | Second company name |
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 carry the full burden. It discloses the tool searches the web and extracts content, but does not mention behavioral traits like rate limits, data freshness, or whether it performs real-time API calls. The pricing information is useful but does not cover behavioral implications.
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 long, front-loading the core purpose first. Every sentence adds value: the first sentence defines the function, the second adds method and cost. No extraneous information.
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 lack of output schema, the description should explain the return format. It says 'provides structured comparison' but does not elaborate on whether it returns a table, JSON, or prose. For a tool with 4 parameters, the description covers the basic workflow but omits details about the structure and limitations of the output.
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% with all parameters described. The description adds minimal context beyond the schema: it explains the optional 'aspect' and 'industry' parameters, but this is largely redundant with the schema descriptions. The description does not clarify how these parameters affect the search or comparison logic.
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 performs competitive intelligence analysis comparing two companies/entities, searches the web, extracts content, and provides a structured comparison. This sufficiently distinguishes it from siblings like 'compare_articles' and 'analyze_text', which focus on comparing articles or general text 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 mentions pricing per call or subscription, but provides no guidance on when to use this tool versus alternatives like 'research_topic' or 'extract_content'. There is no indication of prerequisites, exclusions, or typical use cases. The agent lacks context for choosing this tool over siblings.
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.