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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly implies a read-only operation via 'Retrieve', but it does not mention potential errors, authentication needs, or response format. This is adequate for a simple fetch tool but lacks deeper behavioral context.

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 with no filler. The primary action is front-loaded, and the added sentence explains practical value 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 single-parameter tool with full schema coverage and no output schema, the description is largely complete. It explains what the tool does and why it matters, though it does not specify the shape or richness of the returned field definitions.

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 schema provides 100% coverage for the single parameter, including examples like 'Account', 'Contact', and '1'. The description adds no additional parameter-level meaning beyond reiterating that it applies to 'any object', so the baseline score of 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 ('Retrieve') and resource ('detailed field definitions for any object'), making the tool's function immediately clear. It also distinguishes itself from siblings like get_objects and query by focusing specifically on field definitions rather than objects or record data.

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 clear usage context: it is essential before creating or updating records to know which fields exist. It does not explicitly name when not to use it or list alternatives, but the stated purpose is enough to guide an agent toward appropriate use.

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 must carry behavioral disclosure. 'Discover' implies a read-only listing operation, and 'available' scopes the result to existing modules/objects. However, it does not detail the return shape, any prerequisites, or possible side effects, leaving some ambiguity.

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 crisp sentences with no wasted words; the core action is front-loaded and the usage hint follows immediately. Ideal size for an agent-reading description.

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 discovery tool, the description is largely complete: it states the output concept (available modules/custom objects) and the recommended usage position. It stops short of describing the exact response format, but that is a minor gap given the simplicity.

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?

Tool has zero parameters and the schema confirms this, so the baseline of 4 applies. The description adds no parameter-specific detail, but none is required since there is nothing to configure.

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: 'Discover all available CRM modules and custom objects.' The phrase 'Use this first to understand the system structure' clearly positions it as the entry-level discovery tool, distinguishing it from siblings like query and get_fields.

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 explicit timing guidance: 'Use this first to understand the system structure.' This tells the agent when to invoke it, though it does not explicitly name alternative tools 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?

With no annotations, the description carries the full burden. 'Fetch' implies a read-only operation, and the relationship traversal is clear. However, it does not disclose behaviors such as whether traversal is only direct, whether result lists are paginated, or any error conditions. Minimal but not misleading.

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 filler. The main action is front-loaded, and the example adds immediate, practical clarity. Every word 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?

The tool has three required parameters, no output schema, and no annotations. The description provides purpose and an example but omits parameter definitions, return format, and any edge-case behavior. An agent might infer the mapping, but the description alone is not sufficiently complete for confident 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?

Schema description coverage is 0%, so the description must compensate. The example 'Contacts under an Account' gives an indirect clue that objectType is the source type and relatedObjectType is the target type, but none of the three parameters (objectType, recordId, relatedObjectType) are explicitly explained or mapped to the example.

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 ('Fetch'), a specific resource ('records linked to a specific entity'), and gives a concrete example ('Contacts under an Account'). This clearly distinguishes it from sibling tools like get_objects or query, which serve different purposes.

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 'Useful for traversing relationships' provides clear context for when to use this tool. However, it does not explicitly mention when not to use it or name alternatives like query for arbitrary filtering, so it falls short of full guidance.

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 exist, so the description carries the full burden. It discloses that delete and update operations exist but does not state whether delete is destructive/permanent, what update overwrites, permissions needed, or error behavior. This is a significant transparency gap for a mutating 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 two sentences with no filler. It front-loads the core purpose and then gives conditional parameter details, making it easy to scan quickly.

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 four-parameter mutation tool with no annotations and no output schema, this description is too thin. It misses the required objectType parameter, delete behavior, and any response or error expectations, leaving an agent under-informed for safe and correct use.

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. It partially explains recordId and data, but omits objectType, which is required, and leaves the data object vague. The action enum is self-explanatory, but the overall parameter guidance is incomplete.

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 explicitly states the tool creates, updates, or deletes CRM records, giving a clear verb and resource. It is easily distinguished from the read-oriented siblings like get_objects and query, so an agent can tell this is the mutation 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?

It provides clear action-specific guidance: update requires recordId, and create requires a data object. It does not explicitly name alternatives or exclusions, but the mutation context makes when to use it clear relative to the read-focused sibling tools.

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 must carry the behavioral disclosure burden. It usefully reveals the filter string syntax with the 'contains' example and notes pagination support. However, it does not state whether the operation is read-only, what the response looks like, what errors may occur, or any rate limits or side effects.

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 sentence that front-loads the core action and includes a concrete example. It is lean with no fluff, though it does repeat some parameter names already visible in the schema.

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 a nested input schema, no annotations, no output schema, and no parameter descriptions, the description is too thin to be self-sufficient. An agent is left unsure what objectType should contain, how orderby behaves, and what a successful result looks like.

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. It adds meaningful detail for filter syntax and mentions pageNumber/pageSize, but it omits the required objectType parameter entirely and does not explain orderby or how the nested query object should be structured.

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 uses a specific verb ('Execute') and identifies a clear capability: complex search queries with logical filters and a concrete filter example. However, it never names the target resource ('objects', 'records', etc.) and does not differentiate itself from siblings like get_objects or get_related_records, so it stops 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?

There is no explicit guidance about when to use this tool versus alternatives. The phrase 'complex search queries' implies a niche, but the description never mentions get_objects, get_related_records, or conditions for choosing one over the other.

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