social-research-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'detect trending topics' and 'identifies spikes,' which implies a read-only analysis operation, but doesn't specify whether it requires authentication, has rate limits, returns structured data, or handles errors. For a tool with no annotation coverage, this leaves significant behavioral 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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and key actions. Every sentence earns its place by conveying essential information without redundancy or fluff.

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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It explains what the tool does but lacks details on behavioral traits, usage guidelines, and output format. Without annotations or an output schema, more context on results and constraints would improve completeness.

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 description adds no parameter-specific information beyond what the schema provides. With 100% schema description coverage, the baseline is 3, as the schema already documents all parameters (time_window, platforms, top_n) with descriptions and enums. The description doesn't compensate with additional context like examples or edge cases.

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 purpose: 'Detect trending topics across your stored timeline data' with specific actions like 'identifies spikes in hashtag/keyword frequency and sentiment shifts.' It distinguishes from siblings like timeline_query, timeline_search, and timeline_stats by focusing on trend detection rather than querying, searching, or statistical analysis. However, it doesn't explicitly differentiate from analyze_sentiment, which might also involve sentiment 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. It doesn't mention when to choose timeline_trends over timeline_query, timeline_search, timeline_stats, or analyze_sentiment, nor does it specify prerequisites like requiring stored timeline data. Usage is implied but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.