extract_tables_from_markdown

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains that the parser is resilient and lists specific edge cases it handles. It also discloses the payment requirement (0.01 USDC on Base L2), which is important operational context not visible elsewhere.

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 a single focused sentence followed by a brief parenthetical cost note. It front-loads the core purpose and resilient behaviors, with no filler or redundant content.

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?

For a simple parser with two parameters and no output schema, the description provides enough context to understand what input is expected and what kind of output is produced. It could be more explicit about the exact output shape or the required payment signature, but it is sufficiently complete for an agent to select and invoke the tool.

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?

Although the schema covers both parameters, the description adds meaning by clarifying that the payload is expected to contain markdown table content and that a Base L2 USDC payment is involved. This helps the agent interpret the otherwise generic 'payload' and 'paymentSignature' fields.

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 identifies the tool as a markdown table parser and names specific resilient behaviors: missing pipes, ragged rows, and embedded line breaks. It also states the output type ('structured tables'), making the purpose concrete and distinct from sibling tools like clean_markdown_for_llm or pdf_table_stream_extractor_resilient.

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 when parsing messy or non-standard markdown tables, but it does not explicitly state when to prefer this tool over alternatives. There are no exclusions or references to other tools for comparison, so the agent must infer the appropriate context.

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