query_knowledge_graph

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

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond the annotations: the dependency on build_index, the notion of PageRank-ranked papers, shortest citation paths, hop-based neighborhoods, and month-by-month citation velocity. This helps the agent anticipate what each query actually computes and what state it requires.

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 long but earns its length because it must document ten distinct query types with varying parameter requirements. The most important guidance (when to use and the prerequisite) is front-loaded, and the query-type inventory is dense with useful information rather than padded.

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 — 11 parameters, 10 query types, and a prerequisite — the description covers the key usage dimensions: when to use it, what each query type means, which parameters each requires, and what must happen first. The output schema handles return-value documentation. Minor gaps remain for generic parameters like topic and years, but overall the description is sufficient for an agent to invoke the tool correctly.

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 0%, so the description must compensate, and it largely does by mapping query types to required and optional parameters: path needs doi_a and doi_b, neighborhood needs doi plus optional depth, citation_velocity needs doi, trending takes limit and years, and timeline takes topic/start_year/end_year. It leaves a few generic parameters like response_format and exact date formats implicit, but the core parameter-to-query-type relationships are explained.

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 names a specific action and resource ('Query the knowledge graph') and immediately enumerates ten concrete query types that distinguish it from sibling tools like get_citation_graph or find_related_papers. It also states the exact user intents it serves ('influential papers, research clusters, publication trends, or relationships between papers'), making it unambiguous what this tool is for.

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 gives clear start conditions: 'Use this when the user asks about influential papers, research clusters, publication trends, or relationships between papers,' and it states a hard prerequisite ('Requires build_index(type="graph") to be run first'). It does not explicitly mention when not to use it or name alternative sibling tools, so it falls short of a 5 but is clearly above vague.

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