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, covering the safety profile. The description adds useful scoping with 'published,' indicating this is the public/external profile, but it does not disclose additional behavior such as default link inclusion or output format.

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 filler or redundancy. It efficiently conveys the tool's purpose while leaving parameter details to 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?

For a simple read-only tool with one optional parameter, the description plus schema and annotations are largely sufficient. It does not describe the return shape or explicitly differentiate from siblings, but these are minor gaps for a straightforward profile fetch.

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 parameter include_links is fully documented in the schema with a description and default value, so schema coverage is 100%. The description does not add parameter-level meaning, but it does not need to since the schema already carries that burden.

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 a specific verb and resource: 'Get Sudhanva Narayana's published professional profile and expertise.' It is distinct enough from the sibling tools, which cover case studies and published articles, though it does not explicitly name 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?

Usage is implied through 'published professional profile,' suggesting this tool is for profile-level information. However, there is no explicit guidance on when to prefer this over list_case_studies or list_published_articles, nor any exclusions or alternatives.

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 carry the safety profile (readOnlyHint, idempotentHint, non-destructive). The description adds filtering scope ('published', 'verified') but does not disclose return format, ordering, or any additional runtime behavior beyond what annotations and schema already imply.

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 filler. Every phrase ('published', 'production ML', 'verified outcomes') adds meaningful scope, and the tool's purpose is immediately 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?

For a simple read-only list tool with one optional, well-documented parameter and rich annotations, the definition is nearly complete. It could explicitly state that the response is an array of case studies, but the title and description make that easily inferable.

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 coverage is 100%, so the single 'limit' parameter is fully documented in the schema. The description adds no parameter-specific detail, 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 uses a specific verb ('List') and resource ('published production ML case studies'), and adds qualifiers ('verified outcomes') that clearly distinguish it from generic article listing. The title also identifies the owner ('Sudhanva'), making the tool's purpose unambiguous.

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 guidance or mention of alternatives, but the resource type is distinctive enough that an agent can infer this is for case-study lookups rather than profile or article listing. The context is implied rather than stated, which keeps it at a minimum viable level.

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, so the description does not need to restate safety. It adds domain context ('published', 'technical writing', 'canonical slug') but does not clarify behavior such as combining tag and slug or returning lists versus single objects beyond the schema's slug description.

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 one efficient, front-loaded sentence that communicates the two main usage modes without waste. Every word contributes meaning and the key action is immediately 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?

For a simple read-only tool with annotations covering safety, the description is adequate but not fully complete. It does not explain what happens when no parameters are provided, whether tag and slug are mutually exclusive, or what the return shape looks like, and there is no output schema to fill those gaps.

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 33%, so the description carries more responsibility for parameter meaning. It clarifies tag as a filter and slug as a single-article selector, but it adds nothing about 'limit', which remains undocumented in both the schema and the description. This is a meaningful gap for a tool with no required parameters.

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 ('List') and resource ('published technical writing'), and clearly explains two modes: listing by tag or retrieving one article by slug. It does not explicitly distinguish itself from siblings like list_case_studies, 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?

The description implies when to use each parameter mode: use 'tag' to get a list or 'slug' to get one specific article. However, it provides no explicit guidance about when to prefer this tool over siblings or what to do when no parameters are supplied, leaving usage context mostly implied.

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