smart-data-extractor

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

With no annotations provided, the description carries the full burden and succeeds by disclosing key behavioral traits: idempotency, 30-second timeout, 5-minute deduplication window, and the fact that it does not extract data. This is unusually transparent for a tool without annotations.

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 concise and front-loaded with key traits (Idempotent, 30s timeout) before stating the core function. The additional sentence about idempotency_key is necessary and directly actionable. No unnecessary words.

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 tool with only two parameters and no output schema or annotations, the description covers purpose, behavioral traits, and parameter guidance. It clearly implies the return value is a JSON Schema, making the tool's behavior complete enough for an agent to use 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 coverage is 100% with detailed parameter descriptions already provided in the schema. The description adds no new parameter semantics beyond what is already in the schema, such as the idempotency_key purpose, so it meets the baseline without adding extra value.

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 infers JSON Schema from sample data, with the specific verb 'infer' and resource 'JSON Schema'. It distinguishes from sibling extraction tools via the phrase 'without extraction', making its unique purpose evident.

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 context that this tool is for schema inference rather than data extraction, which implies when to use it versus the sibling tools. It also gives explicit guidance on using the idempotency_key for deduplication. However, it does not explicitly name alternatives or state when not to use it.

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

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

It discloses idempotency, a 30-second timeout, and a 5-minute deduplication window, which are key behavioral traits not otherwise present in annotations or schema. It does not cover error handling or partial failure behavior, which leaves some gaps for an extraction tool with no annotations.

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, scannable sentence with key attributes front-loaded (idempotency, timeout) followed by the core purpose. Every element is purposeful with no 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 absence of an output schema, the description could have hinted at the return type, but it provides essential operational context (timeout, idempotency, dedup). The rich input schema compensates for details about source limits and formats.

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 input schema has 100% coverage with detailed parameter descriptions, so the description adds only marginal value by referencing the consistent schema and idempotency_key. The schema itself fully explains the sources, schema, and idempotency_key semantics.

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 extracts data from multiple sources (JSON/JSONL/text) with a single consistent schema. It uses a specific verb 'Extract' and names the resource, distinguishing it from siblings like extract_from_api or extract_from_url.

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 use for batch extraction from multiple sources with a unified schema, which gives clear context. However, it does not explicitly state when to prefer this tool over alternatives or list exclusions.

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

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 indicates idempotency, a 30s timeout, and a 5-minute deduplication window via `idempotency_key`. These are meaningful behavioral traits beyond what the schema reveals.

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 concise and front-loaded with key information ('Idempotent · 30s timeout') followed by the core purpose. Every sentence adds value, with no redundancy 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?

Given the moderate complexity (3 parameters, no output schema), the description covers the essential purpose and behavioral traits. It could mention the return format or error handling, but the schema already handles parameter details, making the description sufficiently complete for most use cases.

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 each parameter already has a detailed description. The tool description only mentions `idempotency_key` without adding new meaning beyond the schema. Thus, it meets the baseline for high coverage but does not exceed it.

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 states 'Extract structured data from API response JSON with schema adaptation'—a specific verb and resource that clearly defines the tool's function. This differentiates it from siblings like extract_from_url and batch_extract, which target different input sources or processing modes.

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 context for use: API response JSON and schema adaptation. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, so it earns a 4 rather than a 5.

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

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

The description discloses idempotency, a 30s timeout, and a 5-minute deduplication cache, which are behavioral traits not covered by annotations (none provided). It does not discuss error handling, output format details, or authentication requirements, but the disclosed traits are valuable.

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 two sentences, starting with key traits (idempotent, timeout) and then the purpose. Every sentence adds distinct information; no 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?

The tool has 3 parameters, no output schema, and no annotations. The description covers the core extraction behavior and idempotency context, but does not explain the response format, how raw content strings are handled, or error conditions. This leaves the agent with some ambiguity about the tool's full behavior.

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?

All three parameters have schema descriptions with 100% coverage, so the description adds little beyond the schema. It reinforces that omitting 'schema' triggers auto-learning and that 'idempotency_key' enables deduplication, both already in schema descriptions.

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 states 'Extract structured data from URL content with auto schema learning', which is a specific action on a specific resource. It distinguishes from batch_extract by focusing on a single URL, but does not explicitly name alternative tools. The raw content string option is not mentioned in the description, only in the schema.

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 conditional use of 'idempotency_key' to deduplicate calls, but does not explicitly state when to use this tool over sibling tools like auto_schema_learn, batch_extract, or extract_from_api. The 'auto schema learning' hint implies when to omit 'schema', but no direct alternatives are given.

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