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

With no annotations available, the description carries the full burden of behavioral disclosure. It does reveal that the tool is list-oriented and that behavior depends on the server type, but it does not mention return format, pagination, permissions, or error conditions. For a simple read-only list operation this is adequate, if minimal.

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 entire description is one sentence with no filler, leading with the core action and then the one prerequisite an agent needs. Every part of the sentence adds value, making it maximally efficient for a single-parameter tool.

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 a single parameter, no annotations, and no output schema, the description adequately conveys the purpose, the precondition for obtaining dbId, and what the return will roughly be (a list of schema/database names). It could be slightly more explicit about the exact return structure, but nothing essential 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 dbId parameter, with the schema already documenting 'Database server id from list_databases.' The description repeats this dependency almost verbatim, adding no meaningful semantic addition beyond what the schema provides, so the baseline 3 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?

The description names a specific verb ('List') and resource ('schemas' or 'databases') and clarifies the type-dependent behavior (MySQL vs MongoDB). This clearly distinguishes it from siblings like get_tables and list_databases without needing to inspect 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 clear context by stating the prerequisite: 'Use list_databases first to get dbId.' This tells the agent the preceding step needed to invoke the tool correctly. However, it does not explicitly discuss alternatives or when this tool should not be used, so it stops one step short of 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It reveals engine-specific behavior (MySQL tables vs MongoDB collections) and the exact return shape (a map of object name to column/field list), which makes the tool predictable. It does not discuss errors or authentication, but the listing verb clearly conveys a read-only metadata operation.

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 short sentences: the first states the core action and engine variants, and the second states the return format. There is no filler, redundancy with the schema, or unnecessary background information.

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 is simple, the schema fully documents its two parameters, and the description explains the return format in the absence of an output schema. The dual-engine behavior is covered, making it complete enough for an agent to select and invoke the tool 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 description coverage is 100%, so both parameters are already fully documented in the input schema, including their provenance from list_databases and get_schemas. The description adds general context about the output being per-schema, but it does not add per-parameter semantics beyond what the schema already provides.

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 ('List'), resource ('tables->columns' or 'collections->fields'), and scope ('in one schema'). It is immediately distinguishable from sibling tools: list_databases lists servers, get_schemas lists schemas, and run_query executes queries.

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 and parameter names imply the intended workflow: use list_databases to get dbId, get_schemas to get schemaName, then this tool to inspect schema structure. It provides clear context but does not explicitly state when not to use it or mention run_query as an alternative for actual data retrieval.

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 must carry the burden of conveying behavior. It clearly signals a read-only listing operation limited to databases 'the user can access.' It does not mention pagination, failure modes, or what happens when access is missing, but for a simple no-argument list tool the core behavior is 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?

A single sentence that states the action, the restriction, and the output shape. It is front-loaded and contains 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?

For a zero-parameter tool with no output schema, the description covers the essential return elements (dbId, dbType) and the access scope. It does not explicitly note any dependency on login or how the data connects to follow-on tools, but these are minor gaps in a simple command.

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?

There are zero parameters, and schema description coverage is 100%, so there is no parameter meaning left to add. The baseline of 4 applies because nothing needs clarification.

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 an exact operation ('List') on a specific resource ('database servers the user can access') and enumerates the returned fields (dbId, dbType). This cleanly sets it apart from sibling tools like get_schemas and get_tables, whose names indicate different resources.

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 tool is for enumerating accessible databases initially, and the sibling names suggest the subsequent steps. However, it never explicitly says 'use this before get_schemas/get_tables' or when not to use it, so the routing logic is only inferred.

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 discloses the tool's behavior (opens a browser), the trigger condition, and the fact that it's not normally required. It could mention whether it returns anything or blocks, but given the simplicity of the action, it is sufficiently 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?

Two sentences with no fluff. The action is stated first, followed by the condition and the normal alternative. Every word adds value.

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 no parameters and no output schema, the description fully covers what the agent needs to know: what it does, when to call it, and when it's unnecessary. Nothing is missing for correct invocation.

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 is trivially complete. The description adds no parameter semantics because none exist; the baseline of 4 for zero-parameter tools 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 clearly states the action ('Open browser for Webget Google login') and the resource ('Webget Google login'), distinguishing it from sibling database tools. It also mentions the specific condition under which it's needed, making the purpose unambiguous.

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 states when to use ('Only needed if calls fail with auth errors') and when not to use ('normally the first API call auto-triggers this'), providing clear guidance and an alternative path. This is excellent usage direction.

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 present, so the description carries the burden of behavioral disclosure. It usefully discloses return formats (TSV for MySQL, JSON for MongoDB) and the schema-qualification requirement, but it does not mention whether writes are permitted, authentication needs, or error behavior. This is adequate but not comprehensive for a raw-query 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?

The description is compact and well-structured: it states the core purpose first, then gives dialect-specific requirements and output formats. Every sentence contributes necessary information 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?

With no output schema, the description appropriately explains return formats. It also covers the main complexity of the tool, the two query dialects and their syntax constraints. It does not cover side effects or how the server type is determined, but this is a minor gap for a query-execution 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 100%, so the baseline is 3. The description adds meaningful value by explaining what kinds of query text are valid and what the required syntax looks like for each dialect, going beyond the schema's generic 'SQL or MongoDB query text'.

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 action (run a query) and a specific resource (a database server), and immediately distinguishes the two supported dialects: MySQL and MongoDB. Its scope is clearly different from sibling metadata tools like list_databases, get_schemas, and get_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 concrete conditions for both dialects: MySQL queries must start with 'use <schema>;' or use schema-qualified names, and MongoDB queries use db.collection.find()/aggregate() style. It does not explicitly name alternatives or exclusion criteria, but the context is clear enough for an agent to know when this tool is appropriate.

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