Sudhanva Public Profile

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 covered. The description adds 'published' and 'professional,' reinforcing that the data is public and non-sensitive, but it does not disclose additional behavioral details such as output format or whether the profile is cached.

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 that communicates the core purpose without filler. Every word contributes meaning, and no unnecessary detail or repetition weakens it.

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 tool with zero required parameters and no nested objects, the description is largely complete: it names the resource and its scope. The main omissions are explicit guidance on when to use this tool instead of the siblings and what the returned profile structure looks like, but these are minor given the low complexity.

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 include_links parameter already has a clear schema description ('Include canonical profile and social links. Defaults to true.'). The tool description adds no further parameter meaning beyond what the schema provides, 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 ('Get') and resource ('Sudhanva Narayana's published professional profile and expertise'), making the tool's purpose immediately clear. It is naturally distinguished from the sibling tools, which list case studies and published articles rather than the profile itself.

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 should be used when retrieving the profile or expertise, but it does not explicitly state when to use this tool versus the siblings or mention any exclusions. The differentiation from list_case_studies and list_published_articles is left to inference from the resource names.

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, covering the safety profile. The description adds content context (published production ML and verified outcomes) but does not disclose any additional behavioral traits such as ordering, pagination, or limitations. Given the annotations, the description contributes minimal behavioral value beyond the content.

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 concise sentence that front-loads the core purpose without any unnecessary words or redundancy. It is efficient and clear.

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 with one optional parameter and read-only semantics. The description clearly states what it returns, and the schema covers the limit parameter. The only gap is the lack of usage guidance, which is a minor omission for a low-complexity list tool. Overall, the description is sufficiently complete given the annotations and schema.

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 fully describes the limit parameter with a clear description and default. The description does not add any additional meaning to the parameter beyond what the schema already provides. With 100% schema coverage, the baseline 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 clearly states the tool lists published production ML case studies and verified outcomes, using a specific verb and resource. It is distinct from sibling tools like get_public_profile and list_published_articles, which focus on different content types.

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 no guidance on when to use this tool versus alternatives. It does not mention conditions, exclusions, or compare to list_published_articles or get_public_profile, leaving the agent to infer appropriate usage.

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 fully cover the safety profile (readOnlyHint, idempotentHint, destructiveHint), so the description does not need to restate those. The description adds the functional scoping of tag filtering vs. slug lookup, but it does not disclose edge behaviors such as what happens when both tag and slug are provided or how limit interacts with the two modes. This is acceptable basel-old given annotation coverage but adds only minimal 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?

A single, well-structured sentence. The core action ('List published technical writing') is front-loaded, and the two modes are stated succinctly without wasted words. Every part of the sentence earns its place and there is no redundant restatement of the tool name or title.

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 list/retrieve tool with 100% schema coverage and annotations fully describing safety, the description is largely complete. The only minor gap is the lack of explicit guidance on the precedence or exclusion of providing both tag and slug simultaneously. Since the schema allows both, an agent might wonder which wins; this is a small but non-negligible ambiguity.

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%, with each parameter already clearly documented in the input schema. The tool description also names 'tag' and 'canonical slug', which reinforces the parameter meanings, but it does not add new semantic detail beyond what the schema's descriptions already state. Baseline 3 applies because the schema does the heavy lifting.

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 specific verbs ('List', 'retrieve') and clear resources ('published technical writing', 'canonical slug'), and it distinguishes itself from sibling tools by focusing on technical writing articles rather than profile or case-study content. Its two-mode structure (list by tag vs. retrieve by slug) leaves no ambiguity about what the tool does.

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 operational context: use it to list all published technical writing filtered by tag or to fetch a single article via slug. It does not explicitly name alternatives or exclusions, but the sibling tools (get_public_profile, list_case_studies) are obviously different resource types, so the intended selection is apparent. Lacks an explicit 'when not to use' but exceeds an implied-only level.

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