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

With no annotations provided, the description carries the full disclosure burden. It states execution mechanics ('Reads SQLite directly'), that no external dependency exists ('no LLM call, no API key'), and the return format ('Returns JSON'). It also explains a non-obvious behavior — returning real data coverage ranges for date columns so empty results can be distinguished from out-of-range questions. The 'describe' verb implies read-only; the description could state non-mutating explicitly but the disclosed details exceed the baseline for an annotation-less tool.

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 sentences, each earning its place: deliverable list, when-to-use, and execution/return format. The content list is front-loaded ahead of the usage guidance. It is information-dense rather than wasteful, though the long enumerative first sentence could be tightened slightly.

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, so the description bears full responsibility for explaining return content — it enumerates all major elements including the non-obvious date-range feature. One fully-documented parameter, usage routing against siblings, execution behavior, and return format are all specified. Nothing an agent needs to call it correctly is missing.

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% for the single table_name parameter, which the schema already documents as 'Exact table name, as returned by list_tables (e.g. 'orders', 'order_items').' With full schema coverage the baseline is 3; the description adds no parameter-specific detail beyond hinting that the table must exist (it describes 'one table'). The schema does the heavy lifting, so 3 is correct.

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 names a specific verb-resource pair ('Returns everything about one table') and enumerates the exact contents delivered: columns with type/nullability/PK/plain-language, foreign keys, CREATE TABLE, caveats, and date range coverage. It clearly differentiates from siblings — it describes schema rather than querying data (query_database, execute_sql) or listing tables (list_tables).

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 explicit when-to-use instruction: 'Use it before writing SQL against a table you have not queried yet.' This frames it as a prerequisite step and implies the alternative is the SQL/query tools, though it does not name them explicitly or state when-not-to-use. Clear context, slightly implicit on 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. It thoroughly discloses: read-only and SELECT-only enforcement, single-statement limitation, SQLite dialect, row cap of 100 with offset/hasMore paging, no LLM involvement (no key, no cost, exact results), and the revenue convention (counting non-cancelled orders). This is exemplary transparency for a tool that executes arbitrary SQL.

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 longer than a single sentence but every section earns its place: purpose, use case, limitations, and revenue convention are separated and clearly front-loaded. It respects the reader by grouping constraints and providing a concrete example. Slightly verbose but never wasteful.

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?

No output schema exists, so the description correctly explains return format ('structured rows plus column names as JSON') and mentions hasMore. It covers all necessary operational details: single-statement rule, paging, dialect, and domain convention. For a complex SQL tool with no annotations, this is remarkably 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 the schema already documents all three parameters (sql, limit, offset). The description reinforces paging behavior ('use offset to page through more') and the revenue convention, but adds no new parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

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 ('Runs') and resource ('read-only SQL query against the e-commerce SQLite database') and specifies the output shape ('structured rows plus column names as JSON'). It clearly separates itself from sibling tools by instructing to use list_tables and describe_table first when schema is unknown, implying this tool is for querying after schema discovery.

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?

Provides clear when-to-use guidance: 'Use this to answer analytical questions yourself' mentioning joins, aggregates, and multi-step work, and when actual values are needed rather than a written summary. It also gives a prerequisite ('Call list_tables and describe_table first if you do not know the schema') and explicit limitations (SELECT only, single statement, paging). However, it does not explicitly name query_database as an alternative, so the situational contrast is slightly weaker than ideal.

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 discloses that it reads SQLite directly, makes no LLM call, requires no API key, costs nothing, returns immediately, and returns JSON. This is comprehensive behavioral disclosure beyond what the empty schema provides.

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 moderately lengthy but every sentence adds valuable information: primary output, use-case guidance, and technical behavior. It is front-loaded with the main purpose and remains readable without 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?

For a zero-parameter tool with no output schema, this description is entirely sufficient. It covers what the tool returns (table lists, explanations, row counts, columns, relationships, revenue convention), when to use it, and how it executes. Nothing an agent needs to decide to call it is missing.

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 has zero parameters, so the schema requires no explanation. Baseline for 0 params is 4, and the description appropriately does not invent parameter information. No additional semantics needed.

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 a specific verb and resource: lists every table in the e-commerce database, with a plain-language explanation of contents, row count, and column names. It also mentions relationships and revenue convention, distinguishing it from sibling tools like describe_table which likely focuses on a single table.

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?

Explicitly tells the agent to use this first when needing to know what data exists or answer database structure questions. It does not name sibling tools directly or state when not to use it, but the stated context is sufficient for an agent to decide.

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 extensively discloses read-only behavior, refusal of write attempts, a 100-row limit, dependence on an LLM/API key or Ollama, variability between runs, and return format (Markdown tables, numbers read out). This goes well beyond typical disclosure.

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?

Well-structured with clear sections (USE THIS, IT KNOWS ABOUT, RETURNS, GOOD TO KNOW) front-loaded with the core purpose. While lengthy, every sentence adds value and the section headers make it easy to scan. 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?

Comprehensive for a natural-language query tool with no annotations and no output schema. It covers scope, entity data, return format, limitations, prerequisites, and refusal behavior. An agent has all necessary information to invoke it 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 covers 100% of parameters with detailed descriptions. The description adds value by reinforcing plain-language usage and clarifying that numbers are read from text rather than returned as structured data. It also implies the message should be a natural question, which complements 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?

States that it answers questions about the online shop in plain language, covering customers, products, stock, orders, and sales. Clearly distinguishes itself from siblings by noting no SQL, table names, or technical wording are needed, implying execute_sql and schema tools are for different tasks.

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?

Explicitly says 'USE THIS for any question about the shop or the business behind it' and provides numerous examples. However, it does not explicitly name alternative tools or give a when-not-to-use clause; the no-SQL instruction implies but does not explicitly state that execute_sql is for raw queries.

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