NoCodeCSV

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 a key behavioral trait: the CSV text is analysed and discarded and nothing is stored, which tells the agent this is a stateless, non-persisting operation. It does not cover size limits, error modes, or latency, so it is good but not comprehensive.

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, front-loaded with the core action, with the data-retention caveat immediately following. Every clause earns its place and nothing is padded.

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?

There is no output schema, and the description does name the return shape (a written answer covering totals/trends/outliers/comparisons/chart), which is the key thing the agent needs. It stops short of stating input size limits or failure behavior, but for a two-parameter tool it is close to complete.

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 both parameters are already documented in the schema and the baseline is 3. The description reinforces that the question is plain English and that csvContent must contain a header row, but adds no parsing rules, size constraints, or format details beyond the schema.

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+resource (ask a question about CSV data, get a written answer) and even enumerates the answer types (totals, trends, outliers, comparisons, chart recommendation), so the tool's function is unambiguous. It does not, however, differentiate itself from the sibling csv_profile, leaving the agent to infer the split between profiling and question-answering.

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?

Usage is implied: ask a plain-English question when you want an analytical answer. There is no explicit when-to-use vs when-not guidance and the only sibling, csv_profile, is never mentioned, so the agent must guess which of the two to pick.

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. It usefully discloses that execution is local, model-free, deterministic, and always available — meaningful traits an agent cannot get from the schema. However, it says nothing about behavior on malformed or empty CSV, size limits, or what happens if the header row is missing.

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, zero filler. The output fields are front-loaded in the first sentence and the performance/availability rationale follows, so an agent gets the essentials immediately.

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?

There is no output schema, but the description compensates by enumerating the returned fields (counts, names, type guess, blank counts), which is the key information an agent needs. Minor gaps remain around input constraints and error 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 coverage is 100% for the single csvContent parameter, and its description already states 'raw CSV text, including the header row.' The tool description adds no syntax, encoding, or delimiter guidance beyond that, so the schema does the heavy lifting — baseline 3.

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 ('Return a deterministic profile of CSV text') and enumerates exactly what the profile contains: row count, column count, column names, numeric/text detection, blank counts. It does not name the sibling analyze_csv, though the phrase 'without an AI model' implicitly contrasts with an AI-driven analyzer.

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?

Usage is implied rather than stated: 'fast and always available' suggests this is the lightweight deterministic option versus analyze_csv, but the description never says when to prefer one over the other or when-not to use this tool. An agent must infer the routing from the tone of the sentence.

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