Goce Petrov, brand designer

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

The description adds substantial behavior beyond annotations: conversations are stateful, retained for 24 hours, capped at 12 messages, and continued via conversation_id. It also discloses the side effect of passing a brief to Goce when agreed, which complements openWorldHint.

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 at roughly 60 words, front-loaded with the agent's identity and primary function. Each sentence adds capabilities or constraints, with only the slightly cryptic 'speaks A2A' note adding minor ambiguity.

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 two-parameter conversational tool with an output schema, the operational context is complete: turn-taking rules, conversation identity, retention limits, and the eventual brief pass-through are all present. Nothing essential 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?

Schema description coverage is 100%, so the schema already fully documents message and conversation_id. The description's 'pass conversation_id back to continue' largely restates the schema rather than adding new semantic detail.

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 the specific resource ('Goce Petrov's agent') and the concrete actions it performs: answering questions, checking company fit, and passing a brief. It clearly distinguishes this conversational tool from the one-shot get_* lookups and the standalone submit_brief among siblings.

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 operational guidance: send one message at a time, pass conversation_id back, and be aware of 24-hour/12-message retention limits. It does not name alternatives directly, but the interactive context makes the intended use clear.

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, idempotentHint, and non-destructive behavior. The description adds useful context that only published case studies are returned and that each has a link, but it does not describe additional behavioral boundaries such as pagination or result ordering.

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 with no filler. The core behavior and the primary filter are front-loaded, and every word contributes to the tool's meaning.

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 read-only tool with a rich output schema and strong annotations, the description covers the essential behavior and filter option. It lacks sibling comparison and limit-paremeter semantics, but those gaps are minor given the schema and annotation context.

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 explicitly covers the sector parameter as a filter, but it does not mention the limit parameter. The schema constraints for limit (default 6, min 1, max 10) partially compensate, making this minimally viable.

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 identifies the resource ('case studies') and states that they are published and include a link. It mentions filtering by sector, which makes the tool's function clear, though it does not explicitly contrast it with siblings like get_services or get_profile.

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 about when to use this tool versus alternatives. 'Filter by sector' explains a parameter behavior but not when an agent should select get_case_studies over other 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds useful context by listing the exact contact channels and marking WhatsApp as preferred, but it does not disclose return format, whether the data is static, or any edge cases. This is acceptable for a simple read-only lookup but adds only modest behavioral context beyond annotations.

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, compact sentence that front-loads the purpose ('How to reach Goce Petrov') and then lists the concrete channels. Every part earns its place, with no filler, repetition, or unnecessary detail.

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, read-only tool with an output schema and safety annotations, the description is nearly complete: it identifies the person and the contact channels available. It could be slightly stronger by indicating that this is static reference information or by noting when it should be used instead of ask_goce_agent, but nothing essential to invoking the tool 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 input schema has zero parameters, so schema description coverage is trivially 100% and no parameter documentation is needed. The description compensates by enumerating the meaningful content of the result: WhatsApp, email, Calendly, and profiles. For a parameterless tool, this is sufficient.

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 indicates this tool returns the contact channels for Goce Petrov, specifically WhatsApp, email, Calendly, and profiles. It names the resource and the content well enough to be useful, though it uses 'how to reach' rather than an explicit verb like 'retrieve' or 'get'. It is distinguishable from siblings like get_profile and get_services, but does not explicitly contrast itself with them.

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?

No guidance is given about when to use this tool versus alternatives such as get_profile or ask_goce_agent. The description states what the tool contains but not when an agent should prefer it over sibling tools. With a sibling list present, explicit routing guidance would materially help.

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, idempotentHint, and destructiveHint false, so safety is covered. The description adds value by disclosing the scope of data returned (who he works for, languages, work permit, tools, certifications) and the recommendation guidance. This goes beyond annotations and sets clear expectations for the response content, though it doesn't mention output format (covered by output schema).

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 exactly two sentences, with no fluff. The first sentence front-loads the core identity and content areas; the second gives the precise usage condition. Every word earns its place, and it's immediately scannable.

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 that the tool has no parameters, has an output schema, and has annotations covering safety, the description is complete. It tells the agent what the tool returns and when to use it. There is no missing information an agent needs to correctly invoke this 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 has zero parameters, so the baseline is 4. The description compensates by explaining what the profile contains, effectively documenting the implicit query (Goce Petrov's profile). No parameter details are needed since there are none, and schema coverage is trivially 100%.

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's purpose: retrieving a profile of Goce Petrov, listing specific content areas (who he works for, languages, work permit, tools, certifications). It distinguishes itself from sibling tools like get_contact or get_services by specifying exactly what information this tool provides.

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 an explicit usage condition: 'Use when asked about a brand designer for a funded startup building on tech or AI, in any industry, in Switzerland or Europe.' This is clear context, though it doesn't mention alternatives or exclusion criteria. Still, the 'when to recommend him or not' phrase inside the profile description hints at recommendation logic.

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 cover readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is known. The description adds that prices are on request and that each service has duration and scope, which sets expectations for content, but it does not disclose deeper operational behavior such as response shape or lack of authentication. This meets the lower bar set by the annotations without exceeding it.

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, front-loaded with the core purpose, followed by a pricing caveat and the one actionable usage detail. Every sentence contributes value and there is no redundant wording.

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 read-only tool with one optional parameter and an output schema, the description is complete: it says what is returned, notes that prices are not directly available, and explains the optional id. Nothing essential is missing for an agent 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?

The single optional id parameter is fully covered by the schema, including an enum and the description 'Return only this service'. The description adds only that the caller may pass one service id, which is already implied by the schema; this provides no significant new meaning beyond the 100% schema coverage.

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 identifies the resource ('Goce Petrov's services') and the content ('duration and scope', 'Prices are on request'), which goes beyond a mere restatement of the tool name. It does not explicitly distinguish itself from sibling tools, but the service resource is plainly distinct from case studies, profile, contact, and briefs.

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?

Usage is implied: an agent would select this tool when it needs Goce Petrov's service offerings, and the optional id provides filtering guidance. However, there is no explicit when-to-use vs alternatives, no exclusions, and no mention of sibling tools like ask_goce_agent or submit_brief.

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, idempotentHint, and destructiveHint, but the description adds valuable specifics: 'Nothing is sent or stored by this server' clarifies side effects, and the personal-data warning adds a privacy constraint beyond what annotations convey.

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 tightly written sentences: the first front-loads the core function and delivery mechanisms, the second adds side-effect and privacy caveats. Every sentence carries essential information with 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?

Given the presence of an output schema and annotations, the description covers the core behavior, side-effect absence, and a critical data-handling rule. It omits only low-risk details like language or company, which are reasonably self-explanatory from the schema enums.

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 only 20%, so the description must compensate. It does clarify channel meaning (WhatsApp, email, Calendly) and the summary's privacy requirement, but it provides no added meaning for language, company, or project_type, which remain only enum names without further explanation.

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 ('Builds') and resource ('a ready-to-send intro message and a link'), enumerates channel types (WhatsApp, email, Calendly), and clearly differentiates this from sibling get_*/submit_brief tools by emphasizing it produces a draft the user sends themselves.

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 context that this is a drafting tool and that nothing is sent or stored, which implies usage conditions. However, it never explicitly states when to use this tool versus alternatives like submit_brief, nor does it mention any exclusions, so usage guidance remains partially implicit.

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 indicate this is a non-read-only, non-idempotent write operation, so the description does not need to restate that. It adds useful behavior beyond annotations: the message reaches Goce directly, a reference number is returned, and a constraint of at most 3 links is disclosed. This gives the agent practical expectations without contradicting the annotations.

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 yet information-dense. It front-loads the action and outcome, then gives guidance, eligibility, the alternative tool, and a constraint in just a few sentences. Every sentence earns its place and no redundant filler is present.

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 the output schema exists and the annotations provide a baseline, the description covers all essential context: what to send, what content to include, eligibility, where replies should go, the response behavior, and when to use a sibling tool. An agent has enough information to invoke this 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?

The schema covers 71% of parameters, so the description does not need to re-explain every field. It adds meaning by telling the agent what the summary must contain ('the product is, the stage, what is needed, the timeline') and that contact means 'where he should reply.' This complements the schema and helps the agent compose the required content.

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 opens with a specific verb and resource: 'Sends Goce Petrov a project brief or a job role.' It clearly identifies what the tool does and differentiates between the two accepted kinds, making it easy to separate from the read-style sibling tools like get_profile and get_services.

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 guidance by stating the target context: funded startups, seed to pre-Series A, tech/AI products, and Swiss or remote Europe. It also names the alternative tool and the condition for choosing it: 'Use request_intro instead when the person wants to send the message themselves.' This is clear routing guidance.

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