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

With no annotations, the description carries the full burden and does disclose key traits: it returns only metadata (no values), the source is a PII inbox Claude cannot read directly, and outputs are id references. It omits error behavior and any access/permission caveats, so it is strong but not exhaustive.

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, front-loaded sentences with no filler. The most important constraint (no values returned) and the downstream usage hint are both stated compactly.

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?

An output schema exists, so return shape need not be explained, and there are no parameters to document. The description covers what it returns, what it withholds, and how ids are consumed, making it essentially complete for a zero-arg discovery 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?

The tool takes zero parameters, so there is nothing for the description to compensate for; the baseline of 4 applies.

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 specific verb and resource: returns a list of files plus estimated schemas from the PII inbox. It also clarifies the negative scope ('does not return values') and distinguishes itself from siblings by noting files are referenced by id for py_run's inputs.

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 the workflow — describe files here, then pass ids to py_run's inputs — which is useful implied guidance. However, it never explicitly says when to use this versus schema_describe or when it is not needed, leaving the choice to inference.

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?

No annotations are provided, so the description carries the full burden. It does disclose a real constraint (n is capped at 50) and the generation mechanism (Faker), which is useful beyond the schema. It says nothing about side effects, permissions, or what happens on an invalid source, so coverage is only partial.

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?

Two sentences plus a tight Args block; the core behavior and the testing purpose are front-loaded with zero filler. Every line earns its place.

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?

An output schema exists, so return values need not be explained, and both parameters are documented. What remains thin is error/edge behavior (invalid source, exceeding the cap) and confirmation of the read-only, side-effect-free nature of a generator.

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 largely does: it defines 'source' as either a claude schema view name or a files_describe id, and 'n' as the row count with a 50-row maximum. It adds meaningful semantics the bare schema (string/integer) lacks, though it omits the default of 5 that the schema carries.

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 specific verb and resource: returns n rows of synthetic Faker data matching the shape of a given view or file id. The shape-matching behavior is distinctive and lets an agent distinguish it from siblings like sql_run or py_run, though it never explicitly names an alternative tool.

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 phrase 'for tests and script development' gives a clear use context, so usage is implied rather than left blank. However, there is no when-not guidance and no explicit routing to schema_describe or files_describe even though those tools produce the 'source' inputs.

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, the description carries the full burden and does a lot: network-disabled execution, print output discarded, only a small JSON result returned, and hard rejection when PII column values appear in the result. It omits execution limits (timeouts/quotas) and whether the run is side-effect-free with respect to the input files, but the safety-relevant traits are unusually well disclosed.

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?

Purpose is front-loaded in the first line, followed by the script contract (sd.load / sd.result), then the two arguments. Dense but well-ordered; the inline code examples earn their space by defining the required calling convention.

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?

An output schema exists, so return values need no further explanation, and the description still clarifies that only sd.result payloads survive and that print output is dropped. For a sandboxed code-execution tool the main residual gaps are error/failure modes and any timeout 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?

Schema description coverage is 0%, so the description must compensate, and it does for both parameters: script is a path relative to the analysis directory (with an example), and inputs is a list of ids produced by files_describe. Both semantics are supplied even though the schema itself documents neither.

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 a specific verb and resource: it runs scripts from analysis/ against inbox files inside a network-disabled container and returns a small JSON result. It is clearly separable from sql_run and files_describe by the 'execute a Python script' framing, though it never names the sibling it is not.

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?

It implicitly shows the workflow (obtain ids via files_describe, then pass them as inputs) and that scripts must live under analysis/, which is useful context. However it gives no explicit when-to-use/when-not guidance and never contrasts itself with sql_run, so an agent must infer the boundary between running SQL and running a Python script.

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?

No annotations are provided, so the description carries the full burden. It does disclose the important safety traits (PII removed, data is Faker-generated dummy rows, no real data returned), which is genuinely useful context, but says nothing about permissions, result size, or pagination.

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 core behavior is front-loaded in the first sentence, the no-real-data caveat follows immediately, and the single parameter is documented compactly. 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?

An output schema exists, so return values need not be described, and the single parameter is covered. The only real gap is routing guidance against the sql_run/fixture_make siblings, which is minor for such a narrow 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?

Schema description coverage is 0% and there is one parameter, yet the description fully explains it: 'view' selects a specific view and defaults to all views when omitted. That compensates well for the undocumented schema, though it adds no format or naming-convention detail.

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 specific verb+resource: it returns column definitions for the PII-stripped (claude) views plus Faker-generated dummy rows, and explicitly notes it does not return real data. This separates it from sql_run, though it never names that sibling directly.

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 'does not return real data' clause implies this is for schema inspection rather than querying, but there is no explicit when-to-use/when-not guidance and no mention of the obvious alternative sql_run or fixture_make. Usage is left to inference.

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, the description carries the full burden and does disclose the key behaviors: read-only, a row-count cap, and small-cell suppression (a privacy safeguard an agent should know about). It omits error/timeout behavior and the exact meaning of the max_rows ceiling, so it is not fully transparent.

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?

Front-loaded one-line summary followed by structured Args entries; every sentence carries constraint information with no filler. The mixed-language summary slightly hurts scannability for a non-Japanese reader but does not waste space.

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?

An output schema exists, so return values need not be described. The description covers what is executed, the safety profile, and both parameters' constraints; only error handling and the concrete row ceiling are absent.

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 does: it constrains sql (SELECT/WITH prefix, no SELECT *) and explains max_rows as a maximum with a configured upper bound. It still doesn't state the default value or the actual ceiling, leaving some ambiguity.

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 specific verb (execute one SELECT) and a specific resource (the PII-free view of the claude schema), with scope qualifiers (single statement). It does not explicitly distinguish itself from py_run, the other execution sibling, so it falls short of a 5.

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 constraints (must start with SELECT or WITH, SELECT * disallowed, read-only) but never says when to choose this over py_run or schema_describe, nor what to do if the query is rejected. Usage is implied by the constraints rather than stated.

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