analyze_dag

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

With no annotations, the description carries full burden and does so thoroughly. It discloses dagitty_string precedence, error fallback behavior, the static concordance attestation, and the epistemic limitation that outputs are 'conditional on the encoded structure.' This goes well beyond a bare action statement, effectively setting expectations for the tool's behavior.

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 longer than typical but uses a clear structure: a direct result sentence, a clarification of accepted inputs, and a contextual paragraph on use with paper DAGs. It front-loads the core purpose, though the final paragraph is fairly lengthy and could be tighter.

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?

The tool is complex and the description covers input modes, precedence, output blocks, attestation, and limitations, which is robust given the lack of an output schema. The absence of per-parameter details is a minor gap, but overall the description provides a complete picture of what the tool does and when to apply it.

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 covers only dagitty_string (20% coverage), and the description compensates by explaining the two input modes and the precedence rule ('only the dagitty_string is used when present'). However, individual fields like edges, nodes, exposure, and outcome are not explained beyond their names, leaving a significant parameter-semantics gap.

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 opens with a specific verb+resource: 'Returns identifiability status and minimal adjustment sets given the DAG provided,' and enumerates concrete outputs (open backdoor paths, adjustment sets, directed paths). This clearly distinguishes the tool from siblings like check_overadjustment or simulate_data by focusing on causal identification from a DAG.

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 clear when-not guidance: it states the tool 'does not verify that the DAG correctly encodes domain knowledge' and advises surfacing ambiguity when a paper lacks an explicit DAG. However, it never names alternative sibling tools or explicitly says 'use this instead of X', so it falls short of the top score.

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