gtm-tools

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does disclose the return format (both records and per-axis diff with winning slug or tie), but does not mention error handling for invalid slugs, whether the operation is read-only, or any side effects. For a read-only comparison tool, this is adequate but not comprehensive.

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, front-loaded with the main action ('Compare two GTM tools head-to-head'), and each sentence adds value: the second explains the return structure. There is no fluff 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?

With no output schema, the description successfully explains return values: both full records and a per-axis diff listing axes and naming winners. It lacks details about behavior on missing slugs or edge cases, but for a tool with only two simple parameters, this is near-complete.

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%: both parameters are described as 'name or slug,' which fully explains what to pass. The description adds no additional parameter-level meaning beyond the schema, so 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 uses a specific verb ('Compare') with a clear resource ('two GTM tools') and defines the output: returns both full records plus a per-axis diff. This clearly distinguishes it from sibling tools like get_tool, search_tools, and list_changes.

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 use when a head-to-head comparison is needed and specifies the output format. It does not explicitly mention when not to use it or name alternatives, but the nature of the tool (comparing two specific tools) is self-evident, providing clear context for the agent.

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. It discloses that results include verified status, curation, product signals, adoption, and review themes, which are key behavioral attributes. It doesn't discuss side effects (likely none for a get) or rate limits, but the return content transparency is strong.

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, well-structured sentence that front-loads the core action and then lists the specific data fields. It is concise and every phrase adds value.

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 (one parameter, no output schema), and the description covers its purpose and return contents. It doesn't mention error handling or pagination, but those are not essential for a single-get tool. Sibling tools provide additional context for selection.

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 provides 100% coverage for the single parameter, including an example. The description adds no additional parameter-level detail beyond restating 'by name or slug', so it meets the baseline of 3.

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: 'Get one GTM tool by name or slug' with a specific list of included data fields. It distinguishes itself from siblings like search_tools (search) and compare_tools (compare) by focusing on fetching a single tool by identifier.

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 when you have a specific name or slug, contrasting with search_tools for exploratory queries. It doesn't explicitly mention alternatives or exclusions, but the context is clear enough for an agent to select it appropriately.

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 transparency burden. It states the ordering ('newest first') and scope ('recent detected changes'), implying a read-only operation. While it doesn't disclose pagination details or rate limits, the limit parameter and simple nature of listing mitigate the need for more.

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 states the core purpose, ordering, and optional filtering in under 20 words. Every phrase adds informational value with no 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 description explains what the tool lists (change kinds) and its ordering, but lacks any description of the return shape or fields since there is no output schema. Given the simple list nature, it's adequate but leaves ambiguity about what a 'change' contains. Could be more explicit about response contents.

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% for both parameters ('kind' and 'limit'). The description adds minimal semantic value beyond the schema—it restates the 'kind' filter and does not elaborate on 'limit'. Baseline of 3 is appropriate since the schema already documents 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 uses a specific verb ('list') and resource ('changes to tracked tools') with explicit change kinds (pricing, reachability, free-tier, deprecation) and ordering ('newest first'). This clearly distinguishes it from sibling tools like get_tool or compare_tools.

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 clear context: it is for viewing recent detected changes, optionally filtered by kind. It does not explicitly mention when not to use it or suggest alternatives, but the context is unambiguous enough for a straightforward listing tool.

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 the tool "Returns ranked slim results" and that the default sort is by score, which directly informs the agent of output shape and ordering behavior. It also mentions the 'verifiedOnly' filter, implying a quality-checked dataset. Missing details like pagination or error semantics are minor for a read-only search.

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 short sentences, each earning its place: purpose, filter/sort options, return type, and a usage tip. There is no redundant phrasing or filler. It is front-loaded with the primary action and efficiently packs the remaining semantics.

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 7-parameter read-only search tool with no output schema, the description is quite complete. It covers all parameter categories, describes the slim result format, and provides a concrete best-practice query. It could add a note about the 'limit' parameter controlling pagination, but the schema already documents that, so the description does not leave major 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 coverage is 100%, so the baseline is 3. The description adds value by grouping parameters into a coherent filtering/sorting story, explicitly listing the tier enum values (BADGE|listed|DEMOTE) in a parenthetical, and giving a cross-parameter recommendation (category + sort=score). This goes beyond the schema's per-field descriptions.

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: "Search the verified GTM-tools directory." This clearly distinguishes it from siblings like get_tool (single tool retrieval) and compare_tools (comparison). It also enumerates the key capabilities (filter, sort) making the purpose unmistakable.

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 a concrete usage tip: "Use category + sort=score for 'best in category'." It also outlines the filtering and sorting axes, giving clear context on when to use this tool. However, it does not explicitly mention when to prefer alternatives like get_tool or compare_tools, so it stops short of full exclusionary guidance.

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