profile

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

With no annotations, the description must carry the transparency burden. It discloses a key limitation ('no raw email is published') and implies a contact boundary, but it does not describe the return format or any other behavioral traits such as data freshness or access restrictions.

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, front-loaded with the core content ('Current availability'). While it is a fragment rather than a full sentence, it avoids unnecessary words and is appropriately sized for a simple getter.

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?

This is a simple tool with no parameters and no output schema, so the description must explain the return value. It lists the information provided (availability, work scope, contact boundary) and one limitation, but it does not specify the structure or format of the returned data, leaving some 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?

The tool has zero parameters, and the input schema is an empty object. Since parameter semantics are not applicable, the baseline of 4 is appropriate; the description does not need to add parameter details.

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 ('current availability, work scope, and the contact boundary') and the tool name 'get_availability' implies a retrieval operation. However, it lacks an explicit verb and does not distinguish itself from sibling tools like get_profile_summary.

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 usage guidance is provided. The description does not mention when to use this tool instead of alternatives such as get_profile_summary or search_faq, nor does it specify any context or prerequisites.

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 present, so the description carries the full burden of behavioral disclosure. It describes the payload but does not state side effects, permissions, error behavior, or response format beyond the vague 'machine-readable pointers'; the 'get' prefix only weakly implies read-only 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 a single terse sentence with no filler, and the core resource is front-loaded. It loses a point for the slightly awkward noun-phrase structure ('Profile summary:') and lack of a clear verb.

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 parameterless read tool, the description conveys the essential return contents (entity identity, availability, pointers) and is sufficient for an agent to invoke it safely. The lack of an output schema lowers the bar, though usage context remains thin.

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 parameter semantics are not applicable. The baseline of 4 applies because no input guidance is needed and the description does not need to compensate for schema gaps.

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 as a profile summary and enumerates its contents (entity fields, availability, machine-readable pointers), making the tool's purpose clear. It does not explicitly distinguish it from siblings like get_availability, but the scope is still understandable.

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 when-to-use guidance or alternative routing is provided. Since the description mentions availability, an agent may be uncertain whether to call this or get_availability; there is no exclusionary note or context for selection.

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 of behavioral disclosure. It states what data is returned but does not disclose whether this is a read-only operation, whether the slug must be URL-encoded, what happens if the slug is not found, or any rate limits. For a simple read tool, the lack of error/not-found behavior and response format details is a notable gap.

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, information-dense sentence. It front-loads the core purpose ('Full indexed detail for one project by slug') and then lists the included fields. No wasted words.

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, the description covers the main purpose and return contents. However, with no output schema and no annotations, it lacks details on error behavior, response format, and slug semantics. It is adequate but not complete for an agent that needs to handle edge cases.

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 for the single 'slug' parameter. The description says 'by slug' but does not explain what a slug is, how it should be formatted, or where to find it. It adds minimal meaning beyond the schema's bare 'slug' string property.

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 full indexed detail for one project by slug, and enumerates the specific fields included (ownership, builtBy, canonical URL, links, proof count). This distinguishes it from sibling tools like list_projects (which presumably lists projects) and get_proof (which fetches a proof).

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: call this when you need full detail for a single project identified by slug. It does not explicitly state when to use alternatives like list_projects or get_profile_summary, nor does it mention any exclusions or prerequisites. The context is clear enough for a single-project lookup, but no explicit routing guidance is provided.

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 full burden. It discloses that unknown slugs return an error and indicates two modes (default and slug-scoped). This is useful behavioral context, though it doesn't cover return format or side effects (if any).

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, front-loaded with the core purpose, followed by a short error-handling note. No wasted words.

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 1-parameter tool with no output schema or annotations, the description covers the purpose, parameter behavior, and error case. The term 'claim gates' might be unclear to unfamiliar agents, but within the sibling context it is 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?

Schema coverage is 0%, so the description must add meaning. It explains that 'slug' specifies a per-project proof link and that unknown slugs cause an error. This clarifies the optional parameter well beyond the bare 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's function: retrieving verification proof for claim gates, or per-project proof links when a slug is given. This distinguishes it from sibling tools like get_project and get_profile_summary.

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: use for proof retrieval, optionally scoped by slug. However, no explicit when-to-use or alternatives are provided, and there is no mention of when choose this over other getter 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 full burden of behavioral disclosure. It discloses filter behavior, empty-list result for unknown values, the ownership/builtBy fields, and the special case of Frontend Mentor Lab counting as one entry. This is exceptionally transparent for a list 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 four sentences and front-loads the core purpose. The caveat about Frontend Mentor Lab is important and not redundant. It is structured logically and avoids unnecessary verbosity.

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 lack of an output schema, the description covers essential return structure (ownership, builtBy) and behavioral nuances (filtering, case sensitivity, special case). It omits details like sorting or pagination, but these are likely not critical for a 17-item list.

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 explains the 'tag' parameter well (Title Case, case-insensitive) and the AND combination logic, but does not elaborate on the 'lens' parameter beyond the enum values. Partial compensation only.

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 ('List') and a specific resource ('canonical portfolio case studies') with an explicit count (17 entries). It differentiates from siblings like 'get_project' (single project retrieval) by implying a collection operation.

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 explains when to use filters and how they behave (AND combination, case-insensitive matching), and notes that unknown values yield an empty list. It does not explicitly mention alternatives like 'get_project', but the context makes the usage 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?

No annotations are provided, so the description carries the full burden. It usefully discloses case-insensitivity, exact-match ranking, and the substring fallback behavior with a concrete example. However, it does not describe no-result behavior, pagination/limit effects, or return shape, which are meaningful gaps for a search tool.

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 that leads with the tool's core function and immediately follows with the most decision-relevant behavioral nuance. The example is compact and instructive. No wasted words.

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 two-parameter search tool, the description covers matching semantics well enough to invoke it, but the lack of any annotation, output schema, or explanation of the limit parameter leaves the agent with notable gaps around result count and return values.

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 for the two parameters. It gives an example for 'query' that implies the query should be a natural-language question, but it never mentions 'limit', its meaning, or its bounds. Half the parameters remain undocumented in both schema and 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?

States a specific verb and resource: 'FAQ search' with the additional qualifier 'Case-insensitive.' The example also clarifies what type of query produces what result. None of the sibling tools overlap with FAQ searching, so the purpose 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 makes clear this is the tool for searching FAQ content, and none of the listed siblings are search tools. It does not explicitly state when not to use it, but the resource scope is obvious enough that an agent can select it correctly.

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