GoAI Moat Outreach Tracker

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

With no annotations provided, the description carries the behavioral burden. It does so by stating the operation is read-only ('Return') and enumerating what is returned. It does not note caveats such as data freshness or scope, but for a no-parameter stats tool this is sufficient.

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?

One sentence with all critical information front-loaded; no filler or repetition. The list of metrics is compact and 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 description covers the purpose, return type, and content for a simple no-parameter tool, and an output schema exists for return format. It does not discuss alternatives or caveats, but given the simplicity and sibling separation, nothing core 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?

There are no parameters, so the description does not need to elaborate on input semantics. The schema is empty and the description adds no parameter confusion. The zero-param baseline 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 states a clear verb ('Return') and a specific resource ('outreach funnel stats'), and lists the exact metrics ('counts per stage, reply rate, conversion rate'). This distinguishes it from the sibling tools, which are about recording touches and updating stages, not reading statistics.

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 the go-to for funnel statistics, but it doesn't explicitly state when to use it instead of record_touch or update_stage. It also doesn't mention any exclusions or alternative retrieval tools. The usage context is clear enough from the resource name, but it remains implied rather than stated.

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. 'Record' implies a write operation, but it doesn't disclose side effects, whether it creates a new entry or updates an existing company, or how the stage parameter interacts with update_stage.

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 front-loaded sentence with no wasted words. It communicates the essential action 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?

The tool is simple, parameters are fully documented in the schema, and an output schema exists. However, the description lacks usage context and does not resolve overlap with update_stage, making it minimally but not fully 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%, and the schema documents stage defaults/allowed values, channel examples, and required company. The description adds no parameter semantics beyond the schema, so 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 action (record) and resource (outreach touch) with a target (company), making the tool's basic function clear. It doesn't distinguish from siblings like update_stage, but the core purpose is identifiable.

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 guidance on when to use this tool versus get_pipeline or update_stage, and no mention of exclusions. The overlap with update_stage (both can set stage) is not addressed.

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 of behavioral disclosure. It only says 'update', which implies mutation but does not explain side effects, overwriting behavior, permissions, reversibility, or what happens to existing data. This is a minimal disclosue for a state-changing 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 a single front-loaded sentence with no wasted words. It is concise and to the point, though it remains thin on behavioral context.

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 simple two-parameter mutation with full schema coverage and an output schema, the description is minimally adequate. It states the core purpose and the schema covers parameters, but missing usage guidance and behavioral transparency make it not fully complete for an agent deciding when and how to use it.

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 no parameter meaning beyond what the schema already provides, and the schema fully documents 'company' and 'stage' with allowed stage values.

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 tool updates a company's stage in the funnel, with a specific verb and resource. It doesn't explicitly distinguish from siblings, but the update action is obviously different from get_pipeline (read) and record_touch (log).

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 guidance on when to use this tool versus alternatives like get_pipeline or record_touch. No conditions, prerequisites, or exclusions are provided, leaving the agent to infer usage from the name and description.

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