GoNoGo

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description still earns credit by disclosing the response shape (verdict + score, or 'research is still running') and by pointing out that full reports live on the project page rather than being returned here — useful non-obvious behavior.

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, with the primary trigger and the return payload front-loaded and the exclusion placed last. Every clause carries 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?

For a single-parameter read tool with an output schema and full annotation coverage, the description supplies the decision criteria, the returned verdict semantics, and the boundary against the sibling interview tool. 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 0%, so the description technically carries the burden, but it says nothing about project_id beyond implying a project is selected. The parameter name is largely self-explanatory and the schema supplies a format pattern, so this is adequate rather than deficient, but no added meaning is provided.

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 ('get ... result' for a GoNoGo project) and enumerates exactly what is returned: the GO / NO-GO verdict and score, or a still-running status. It also names what it is not ('Do not use to start a new interview'), which cleanly separates it from the interview_with_alex sibling.

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?

Gives an explicit trigger ('when the user asks for the verdict, score or research status'), a concrete example scenario ('after finishing an interview with Alex'), and an explicit exclusion ('Do not use to start a new interview'). The routing decision is fully determined without opening any schema.

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?

Annotations only declare the generic mutation profile (readOnlyHint=false, idempotentHint=false); the description goes well beyond them by disclosing side effects (creates a project on the user's GoNoGo account), the non-idempotent one-interview-per-idea rule, and the interaction protocol (user taps Start and talks, the agent must stay silent while the interview runs). These are exactly the behavioral facts an agent cannot derive from structured fields.

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 paragraph is front-loaded with the decisive when-to-use clause and closes with exclusions and the interaction rule, so an agent can stop reading early. It is somewhat long and repeats the problem/payment theme across sentences, keeping it just short of maximally tight.

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 an output schema present, return values need no explanation, and the description still covers the important remainder: side effects (project creation), cardinality constraint, agent behavior during the interview, and the research/verdict that follows. Nothing needed to invoke or orchestrate this tool 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%, so both parameters (idea, language) are already documented with format constraints (maxLength 500, ISO 639-1 pattern). The description mentions the idea is in the user's words and implies the chat language, but adds no syntax or default detail beyond the schema; 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 states a specific verb+resource (opens a live voice interview with Alex, GoNoGo's AI mentor) and names the concrete outcome (market/competitor research plus a GO / NO-GO verdict with reasons). It is unmistakably distinguishable from the siblings get_project_result, list_my_projects and whoami, which are account/result lookups.

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 gives explicit triggering scenarios (validate a startup idea, decide whether to quit a job, rehearse investor/accelerator questions) and an explicit exclusion list (running an existing business, business plans, naming/branding, coding, legal/tax/investment advice). It also resolves the multi-idea case: one interview per idea, run one for each.

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?

The annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile needs no restatement. The description adds domain context about what the listing contains (verdicts, run status) but no behavioral detail such as pagination, result volume, or auth scope. With annotations covering the safety surface, a 3 is appropriate.

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, with the primary trigger stated first and the exclusion last. Every clause earns its place; nothing restates the tool name or annotations.

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 zero parameters, an output schema present, and annotations covering safety, the description carries the only remaining burden: routing. It handles the interview_with_alex boundary but is silent on when to prefer this over get_project_result for a specific project's details, a minor routing gap for a low-complexity 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?

The tool takes zero parameters, so the baseline is 4 and there is no parameter syntax for the description to clarify. Nothing in the description misleads about inputs, though it also adds no implicit filtering semantics (e.g., whether results are scoped to the authenticated user).

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 resource (the user's GoNoGo projects) and enumerates the exact content the tool surfaces: tested ideas, GO/NO-GO verdicts, and running research. Combined with the tool name, an agent can distinguish this list tool from get_project_result and interview_with_alex without opening any 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?

It gives concrete user-utterance triggers ('asks about their GoNoGo projects... verdicts... whether research is still running') plus an explicit exclusion ('Do not use to start a new interview'), which routes the agent away from interview_with_alex. This is explicit when-to-use and when-not-to-use guidance, not inference.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered and an output schema handles return values. The description's only added behavioral detail is that the account is the one 'connected to ChatGPT,' which clarifies the auth/session scope but nothing about failure modes when no account is linked.

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 with no filler, and the resource ('GoNoGo account') is front-loaded immediately after the verb. Every word carries 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?

For a zero-parameter identity tool with an output schema and full annotation coverage, the description is essentially sufficient. It could add one clause on what happens when no GoNoGo account is connected, which is the only realistic edge case an agent would need.

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 takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The description correctly implies no input is required.

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 and resource: 'Returns the GoNoGo account.' This is clearly distinguishable from list_my_projects and get_project_result, which operate on projects rather than identity. It stops short of 5 because it doesn't explicitly contrast itself with the siblings that also return account-scoped 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?

There is no explicit when-to-use or when-not-to-use guidance and no alternatives are named. However, 'whoami' plus 'account connected to ChatGPT' makes the intended invocation context (establishing caller identity) strongly implied, which lands at minimum-viable rather than absent.

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