Scholar Feed MCP Server

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it specifies the scope ('Only includes papers with absolute numeric results'), data source ('Powered by 59k+ extracted benchmark results across 20k+ datasets'), and return format ('top methods/models ranked by score'). However, it doesn't mention pagination, error cases, or performance characteristics.

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?

Three sentences with zero waste: first states core purpose, second specifies constraints and return format, third provides data source context. Every sentence adds value, and the description is appropriately sized for this tool's complexity.

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 annotations and no output schema, the description provides good context about scope, data source, and return format. However, it doesn't describe the structure of returned leaderboard entries (e.g., columns, score formats) or error handling for invalid datasets. For a read-only query tool, this is mostly complete but has minor gaps.

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 schema fully documents all three parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples of metric-dataset relationships or limit implications). Baseline 3 is appropriate when schema does the heavy lifting.

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 with specific verbs ('Get', 'Returns') and resources ('SOTA leaderboard for a dataset/benchmark', 'top methods/models ranked by score'). It distinguishes from siblings by focusing on leaderboard retrieval rather than paper search, citation analysis, or author lookup.

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 usage context by mentioning 'dataset/benchmark' examples and 'absolute numeric results', but doesn't explicitly state when to use this vs. alternatives like 'get_benchmark_stats', 'search_benchmarks', or 'get_paper_results'. No exclusions or prerequisites are provided.

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