Analyze sentiment of text with entity extraction, confidence scores, and key phrase identification. Returns positive/negative/neutral/mixed with detailed breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze for sentiment |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since annotations are empty, the description carries the burden. It discloses that the tool returns sentiment labels (positive/negative/neutral/mixed), includes entity extraction, confidence scores, and key phrases, and provides a detailed breakdown. However, it does not define the exact output structure or any limitations, which prevents a higher score.
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-loaded with the core action and followed by output summary. Every sentence adds value without redundancy.
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?
For a simple one-parameter tool with no output schema or annotations, the description provides sufficient information about what the tool does and returns. It could be more detailed about output structure, but the simplicity of the tool makes this description reasonably complete.
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?
The schema already describes the only parameter 'text' with 100% coverage, so the description's repetition that it analyzes text adds no additional semantics. The baseline of 3 applies because the schema handles parameter documentation.
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's function: 'Analyze sentiment of text' with specific additional features (entity extraction, confidence scores, key phrase identification) and expected output categories. This distinguishes it from sibling tools like extract_structured_data or generate_content.
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 implies the tool is for analyzing sentiment but does not provide explicit guidance on when to use this versus alternatives, nor does it mention any exclusions or prerequisites. The context is clear from the name and description, but there is no explicit usage direction.
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: sentiment analysis, structured data extraction, content generation, health check, tool listing, code review, web scraping, screenshot, and web search. There is minor overlap between scrape_url and extract_structured_data, but the different outputs (raw text vs. structured JSON) make them easily distinguishable.
Most tool names follow the verb_noun snake_case pattern (e.g., analyze_sentiment, scrape_url). The only outlier is 'health', which is a single noun rather than a verb_noun like 'check_health'. This minor inconsistency slightly reduces coherence.
With 9 tools, the server is well-scoped for a general-purpose utility toolkit. Each tool serves a useful function without redundancy, and the count is within the ideal range (3-15) for clarity and manageability.
The tool set covers a broad range of common AI/automation tasks such as text analysis, web scraping, content generation, search, and code review. While some potential utilities (e.g., translation or file conversion) are missing, the lack of a specific domain makes the set feel reasonably complete for a general-purpose toolkit.