talent

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

With no annotations provided, the description carries the full burden. It discloses the side effect of creating a shortlist if it does not exist and mentions the API key requirement. However, it does not describe what happens on duplicate additions, potential errors, or reversibility. The description adds some useful behavioral context but leaves gaps in a mutation tool's 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?

The description is two sentences long, front-loaded with the primary action, and each sentence adds value: the first explains what the tool does and its side effect, the second provides usage context and a prerequisite. No filler or redundancy.

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 (2 params, no output schema, no nested objects). The description covers its purpose, when to use, side effects, and authentication requirements. For the tool's low complexity, this is sufficient context for an agent to select and 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 input schema documents both parameters (handle and shortlist) with descriptions and a default for shortlist, achieving 100% schema description coverage. The description itself does not add further parameter-level detail beyond the schema, except implying that the shortlist parameter can be a new name due to auto-creation. The baseline of 3 is appropriate 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 the specific verb 'Add' with the resource 'candidate to one of your d.cv shortlists', clearly distinguishing it from sibling tools like search_talent (searching) and save_search (saving searches). It also adds a clarifying side note that the shortlist is created if it does not exist, making the tool's function 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?

The description explicitly states 'Use after search_talent when the user wants to save someone', giving a concrete trigger and workflow context. It also notes the API key requirement, which is a prerequisite. This provides clear guidance on when to use the tool, though it does not mention exclusions or alternatives beyond the implicit contrast with search_talent.

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 full responsibility. It discloses the output format (markdown), the profile scope (single), and the source of the id (search handle). It does not explicitly state error behavior or read-only nature, but 'fetch' strongly implies a non-destructive operation.

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 contains no filler. Every phrase adds value: the action, the resource, the format, and the parameter origin.

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 one-parameter getter tool, the description covers the purpose, the parameter provenance, and the return format. No output schema exists, so the description appropriately explains what the agent will receive (full markdown). This is sufficient 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?

The schema already covers the 'id' parameter with an example, and the description adds crucial semantic context by explaining the id is the handle returned from search. This goes beyond the schema's basic type description.

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 fetches a single d.cv profile in full markdown format by id. It uses a specific verb ('Fetch') and a specific resource ('d.cv profile'), distinguishing it from search and shortlist tools. The scope is 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?

The description implies the tool is used after search by mentioning 'the handle returned from search', but it does not explicitly state when to use this over the sibling tool 'get_profile' or provide exclusion criteria. The context is present but not fully explicit.

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 burden of disclosing behavioral traits. The verb 'List' implies a read-only operation, and the additional context 'currently open to work' clarifies the specific subset being returned. It does not describe response format or pagination, but the simple nature of the tool makes this adequate.

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 two short sentences with no redundant wording. It front-loads the action and object, making it immediately understandable.

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 simple two-parameter tool with no output schema, the description provides sufficient context: purpose, optional filter, and usage timing. It does not specify return fields, but for a simple list tool the tool name and description imply the result set adequately.

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 already covers both parameters (limit, skills) with full descriptions. The tool description adds no new semantic value beyond restating that filtering by skills is optional, so the baseline score of 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 clearly states the tool's function: 'List candidates who are currently open to work' with an optional skills filter. This specific verb-resource pairing distinguishes it from sibling tools like search_talent and search, which are broader in scope.

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 phrase 'Best when the user wants people actively looking' provides clear context for when to use this tool, but it does not explicitly name alternatives or provide exclusion criteria. This is a clear context signal without full when/when-not detail.

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?

No annotations are provided, so the description carries the full burden. It discloses that the output is 'clean markdown' and that the profile is returned 'in full,' which gives some behavioral insight, but it does not mention authentication, rate limits, or what 'full' entails beyond that.

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 sentence of 12 words, front-loading the action, resource, and output format. Every word contributes, 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?

For a single-parameter read tool without an output schema, the description tells the agent it will receive a full profile as clean markdown, which is sufficient. It could be slightly more explicit about error cases or the exact content of 'full,' but overall it's adequate.

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 already provides 100% coverage for the handle parameter with an example. The description mentions 'by handle' but adds no additional semantic detail beyond the schema.

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 the specific verb 'Read' and identifies the resource as 'a single candidate profile in full as clean markdown, by handle.' This clearly distinguishes it from sibling tools like search or search_talent, which broadly retrieve candidate 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?

The description implies usage when a complete profile is needed for a known handle, but does not explicitly state when to prefer this over siblings like fetch or search, nor does it mention exclusions.

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?

No annotations are provided, so the description carries the burden. It discloses that the tool saves data (a write operation) and requires an API key. However, it does not explain side effects such as whether the search is overwritten if the same name is used, or what the response contains.

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, front-loaded with the core purpose and a necessary prerequisite. Every word earns its place with no extraneous 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 relatively simple save operation with fully documented parameters, the description is sufficient. The absence of an output schema is not critical here, though it could mention what the tool returns or whether names must be unique.

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 (name, query, open_to_work) already explained in the schema. The description does not add any additional 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 clearly states the action: 'Save a talent search to your d.cv account' with the purpose of re-running later. It distinguishes from siblings like search and search_talent by focusing on saving rather than executing a search.

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 this tool: when you want to persist a search for future use. It mentions the prerequisite of the d.cv API key, but does not explicitly state when not to use it or reference alternative 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It states that results are lightweight and include id, title, and url, and implies a read-only operation. It does not cover pagination or rate limits, but for a simple search tool this is adequate.

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 three short sentences plus a parenthetical, all providing useful information with no redundancy. It is front-loaded with the search purpose and returns format.

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 parameter and no output schema, and the description covers its functionality, return format, and a recommended next step. This is sufficient 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 parameter 'query' is fully described in the schema, and the tool description adds meaningful context about what can be searched (name, role, skills, location). This goes beyond the schema's example, justifying a score above baseline.

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 searches talent profiles by free-text query, specifying searchable fields (name, role, skills, location). It is specific and concise, though it does not explicitly differentiate from sibling 'search_talent', so slightly below 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 gives clear context for using the tool (free-text search) and directs users to 'fetch' for full results. However, it does not provide explicit when-not-to-use guidance or mention alternatives like 'search_talent', leaving usage boundaries 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?

With no annotations, the description carries the transparency burden. It discloses that only published profiles are returned and lists the output fields, but it does not mention pagination, result ordering, auth requirements, or any quirks. This is adequate for a read-only search but not rich.

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, direct and informative. Every clause adds value: the search criteria, the resource, and the return fields. No filler or redundancy.

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 full schema coverage, so the description covers the core purpose and return shape. However, it lacks explicit guidance on choosing this over sibling tools and does not mention pagination limits beyond the limit parameter. Acceptable but not comprehensive.

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 fully documents all four parameters. The description adds a high-level mapping of search criteria but does not clarify how 'availability' maps to the open_to_work parameter. Minimal extra value beyond schema.

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 finds published candidate profiles on d.cv with specific search criteria (keyword, skill, role, location, availability). It also lists return fields, distinguishing it from siblings like get_profile (individual profile) and generic search.

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: when searching for candidates by the listed attributes. It provides clear context but does not explicitly exclude alternatives or name sibling tools, so it misses a bit on when-not-to-use guidance.

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