Multi-source research synthesis on any topic. Searches the web and synthesizes findings with AI. Pay per call (0.02 USDC) or use subscription.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | standard | |
| query | Yes | Research query or topic |
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?
The description discloses that it searches the web and synthesizes with AI, and mentions cost, but does not explain behavioral traits like return format, caching, or limitations. With no annotations, this is insufficient for transparency.
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, front-loads the purpose, and is free of fluff. However, it could be slightly improved by including more detail 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 two parameters, the description is too brief. It does not explain return format, pagination, or how synthesis results are presented, leaving the agent underinformed for a research 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 description coverage is 50% (query has description, depth does not). The description adds no additional meaning beyond the schema; the 'depth' parameter with enum values is not explained at all.
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 performs 'multi-source research synthesis on any topic' with web search and AI, providing a specific verb and resource. However, it does not explicitly differentiate from siblings like 'daily_brief' which also does synthesis, so it lacks sibling distinction.
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 cost but provides no guidance on when to use this tool versus alternatives like 'competitor_intel' or 'compare_articles'. No when-not-to-use or context for selection is given.
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.