netintel_schema_parse

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

With no annotations, the description carries the full burden. It usefully discloses that extraction is LLM-backed and driven by a caller-supplied schema, hinting at nondeterminism, but it says nothing about failure modes, schema-feature limits, permissions, or cost/latency of the LLM call.

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 short sentences, front-loaded with the core capability. The final illustrative clause is slightly redundant but still earns its place as a hint of scope. No padding or filler.

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?

With no annotations and no output schema, and a nested target_schema object that is completely undocumented, the description is adequate but thin — it never addresses what happens when the text doesn't match the schema, nor how nested/typed output is shaped.

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. It does explain both parameters at a high level — raw_text is 'unstructured text' and target_schema is 'any JSON Schema' returning a typed object — but adds no format, size, or structural constraints for either.

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?

States a clear verb+resource (extract structured data from unstructured text) and the mechanism (LLM, arbitrary JSON Schema), which distinguishes it from fixed-purpose siblings like netintel_extract_invoice or netintel_extract_resume. It stops short of naming any sibling it competes with (e.g. netintel_text_to_json, netintel_extract), so an agent must infer the boundary.

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 'works on emails, invoices, resumes, contracts, anything' phrase implies a broad, schema-driven use case, but there is no explicit when-to-use vs. the many specialized extract tools, and no when-not guidance. Usage is inferable but not directed.

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