Scholar Feed MCP Server

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it returns individual entries ordered chronologically, specifies the data format (paper, date, score, value_string), and clarifies that it provides raw scatter data without trend lines or interpretation. However, it lacks details on error handling, rate limits, or pagination.

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, with every sentence earning its place: the first sentence states the purpose, the second details the return format and constraints, and the third provides usage guidance, all 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?

Given the tool's complexity (simple read operation with 2 parameters), no annotations, and no output schema, the description is mostly complete by covering purpose, usage, and return data structure. However, it could improve by mentioning output format specifics or error cases, slightly reducing 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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema by implying the parameters are used to fetch data over time, but it doesn't provide additional syntax or format details, meeting the baseline for high schema coverage.

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 raw benchmark score data points over time') and resources ('dataset+metric'), distinguishing it from siblings like 'get_benchmark_stats' or 'get_leaderboard' by emphasizing raw scatter data without trend lines or interpretation.

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?

It provides explicit guidance on when to use this tool ('Use search_benchmarks first to find the exact dataset and metric names') and implies when not to use it (e.g., for trend analysis or interpretation), effectively differentiating it from alternatives like 'search_benchmarks' for discovery.

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