Releases

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool returns union fields based on entry kind, which goes beyond the schema but doesn't detail potential errors or edge cases. With annotations covering the safety profile, a 3 is appropriate.

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-loading the core purpose and then providing usage context. Every sentence adds value with no waste.

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 single-parameter input, full schema coverage, and strong annotations, the description is complete. It explains what the tool returns and when to use it, covering all necessary context.

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% with a clear parameter description. The description reiterates the identifier accepts slug or ID formats, adding value by clarifying both product and source ID formats, but does not significantly extend 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 clearly states the tool retrieves detail for a single catalog entry by identifier, covering both products and sources. It explicitly distinguishes from likely siblings (e.g., get_product, get_source) by noting it returns the union of product/source fields depending on entry kind.

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 explicit when-to-use guidance: 'Use after list_catalog or search when the user wants to go deep on one entry.' This clearly distinguishes its role from sibling tools like list_catalog and search.

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 and idempotentHint. Description adds value by specifying that hidden/on-demand orgs never leak in the member list, which is a behavioral trait beyond annotations.

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, front-loaded with purpose, followed by a critical behavioral 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 one-parameter tool with no output schema, the description covers what is returned (name, description, member list with filtered orgs). Could mention response format but 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 100% and the description does not add significant meaning beyond the schema's slug description. Baseline 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?

Clearly states 'Detail for a single collection' with specific verb and resource, and distinguishes itself by mentioning collection-specific details like the ordered list of member organizations and the filtering of hidden/on-demand orgs.

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?

Implied usage for getting collection details, but lacks explicit when-not-to-use or alternatives among siblings like get_catalog_entry or get_collection_releases.

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?

Beyond the annotations (readOnlyHint, idempotentHint), the description reveals important behavioral details: cursor stability under inserts, pagination meta structure, and that the response echoes nextCursor for easy chaining. These details are not captured in annotations and add significant transparency.

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 concise and well-structured: a single-purpose opening sentence comparing to a sibling, followed by clear pagination instructions. Every sentence serves a distinct purpose without redundancy, achieving high information density within a few lines.

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 tool has 4 parameters, no output schema, and annotations covering safety, the description provides sufficient context for correct usage. It explains pagination mechanics and references the shape of a sibling tool for response structure. However, a brief note on the response content (e.g., fields returned) would make it fully 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?

While the input schema provides full descriptions for all 4 parameters (100% coverage), the description adds value by explaining the default behavior for 'limit' (default 20) and 'include_prereleases' (defaults to false), and the semantics of 'cursor' (stale cursors silently ignored). This goes beyond the schema definitions, justifying a score above 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 defines the tool as an 'interleaved cross-org release feed for a collection', specifying its scope and distinguishing it from the sibling tool 'get_latest_releases'. It conveys a specific verb ('get') and resource ('collection releases') with precise context.

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 compares this tool to 'get_latest_releases', indicating when to use each based on scope. It also provides practical usage guidelines for pagination, including default limit, cursor usage, and cursor stability. This helps the agent select the correct tool and invoke it properly.

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 mentions filtering but doesn't describe return format, pagination, rate limits, authentication needs, or what 'most recent' means (e.g., time-based or count-based). This leaves significant gaps for a tool with 4 parameters and no output schema.

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, efficient sentence that front-loads the core purpose ('Get the most recent releases') and adds optional filtering details. There is no wasted language, and it's appropriately sized for the tool's complexity.

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 no annotations, no output schema, and 4 parameters, the description is incomplete. It lacks details on return values, error conditions, behavioral traits (e.g., default sorting), and differentiation from siblings. For a read operation with filtering options, more context is needed to guide effective use.

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 4 parameters. The description adds minimal value by mentioning 'product or organization' filtering, but doesn't provide additional context beyond what's in the schema (e.g., explaining relationships between parameters). Baseline 3 is appropriate as 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 clearly states the verb 'Get' and resource 'most recent releases' with optional filtering capabilities. It distinguishes from siblings like 'get_release' (singular) and 'search_releases' (broader search) by focusing on 'most recent' releases. However, it doesn't explicitly differentiate from 'get_source_changelog' which might also provide recent 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 usage through the phrase 'optionally filtered by product or organization,' suggesting when filtering might be applied. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'search_releases' or 'get_source_changelog,' nor does it mention any prerequisites or 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 full burden. It describes what information is returned (accounts, tags, sources, etc.) and mentions the AI-generated overview preview, which adds useful context about the tool's behavior. However, it doesn't disclose other behavioral traits like error conditions, authentication requirements, rate limits, or response format details.

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 with zero waste. The first sentence efficiently lists all returned data fields. The second sentence provides crucial sibling tool differentiation. Every word serves a clear purpose in helping the agent understand and use the tool correctly.

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 operation with no annotations and no output schema, the description does well by specifying exactly what data is returned and differentiating from a key sibling tool. However, it could be more complete by mentioning the response structure or format, given the absence of an output 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?

Schema description coverage is 100%, so the schema already fully documents the single 'identifier' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate coverage through structured data alone.

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 verb 'Get' and the resource 'detailed information about a single organization', specifying the exact data fields included (accounts, tags, sources, products, aliases, AI-generated overview preview). It distinguishes from sibling 'get_organization_overview' by noting the latter provides the full overview text instead of just a preview.

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?

Explicitly states when to use an alternative tool: 'Use `get_organization_overview` to read the full overview text.' This provides clear guidance on tool selection between these two similar tools, though it doesn't mention other siblings like 'list_organizations'.

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 indicates this is a read operation ('Detail for...'), which is helpful, but lacks details on error handling (e.g., what happens if the product doesn't exist), rate limits, authentication needs, or response format. The description adds some context about what data is returned but misses key behavioral traits.

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 core purpose and followed by usage guidance. Every sentence earns its place by clarifying the tool's function and when to use it, with no wasted words 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?

Given the tool's moderate complexity (single parameter, read-only operation) and lack of annotations or output schema, the description is adequate but incomplete. It covers purpose and usage well but omits details on error cases, response structure, or limitations, which would be helpful for an agent to use 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?

Schema description coverage is 100%, with the parameter 'identifier' documented as 'Product slug (e.g., 'nextjs') or prod_ id'. The description adds no additional parameter information beyond what the schema provides, such as format examples or constraints. Baseline is 3 since 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 clearly states the specific action ('Detail for a single product') and resource ('product'), distinguishing it from siblings like 'list_products' (which lists multiple) and 'get_organization' (which focuses on organizations). It explicitly mentions the included data fields (organization, category, tags, sources), making the purpose highly specific and differentiated.

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 explicit guidance on when to use this tool ('when the user asks about a specific product') and gives a concrete example (e.g., 'what sources does Next.js have?'). It also implicitly distinguishes it from alternatives like 'list_products' (for listing all products) and 'get_organization' (for organization-level details), though it could be more explicit about sibling 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?

No annotations are provided, so the description carries the full burden. It describes the tool as a read operation ('Fetch', 'read'), implying it's non-destructive, but does not specify permissions, rate limits, or response format. The description adds some behavioral context (e.g., quoting release notes) but lacks comprehensive details for a tool with no annotation coverage.

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 front-loaded with the core purpose and efficiently structured in two sentences. Every sentence adds necessary information: the first defines the tool's function, and the second provides usage context and parameter details, with zero 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?

Given the tool's low complexity (1 parameter, 100% schema coverage) and no output schema, the description is adequate but has gaps. It explains the purpose and parameter usage well but lacks details on behavioral aspects like error handling or return values, which would be helpful since no annotations or output schema are provided.

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 description coverage is 100%, so the baseline is 3. The description adds value by explaining the parameter semantics: it clarifies that release ids come from other tools and specifies the two acceptable formats (full 'rel_<nanoid>' or bare nanoid), enhancing understanding beyond the schema's basic 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 verb ('Fetch') and resource ('full content of a single release by id'), making the purpose specific and unambiguous. It distinguishes this tool from sibling tools like search_releases and get_latest_releases by emphasizing it retrieves a complete single entry rather than lists or searches.

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 on when to use this tool: after obtaining release ids from search_releases or get_latest_releases, to read the whole entry. It does not explicitly state when not to use it or name alternatives for similar tasks, but the context is sufficient for effective 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 already indicate readOnly, idempotent, and non-destructive behavior. Description adds context about how entries are organized (products vs sources) but doesn't cover behavior like pagination or result size limits.

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?

Description is concise: three sentences cover purpose, scope, and behavioral nuance. No wasted words, though the second sentence could be slightly tighter.

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 schema and annotations, this description provides enough context for an agent to decide when to use it. It explains the derived 'kind' field and when entries appear as products vs sources.

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% with a single optional parameter, and the description does not add new meaning beyond 'scope to organization'. Since schema already covers it, baseline 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 it lists catalog entries combining products and sources, with a discriminator per row. It distinguishes itself from list_products and list_sources by noting it replaces separate calls.

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?

It explains when to use this tool over sibling alternatives (replaces separate list_products and list_sources), but lacks explicit when-not-to-use or context about other 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds pagination details (default 50, page parameter usage), which is valuable but not extensive.

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?

Three efficient sentences: definition, alternatives, pagination. No wasted words, front-loaded purpose.

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?

Provides sufficient context for a simple listing tool: purpose, alternatives, pagination. No output schema, but return format is implied. Enough for correct use.

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 covers 100% of params with descriptions. Description reinforces defaults (50 entries, page 2 for next slice) and provides usage context, adding 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?

Clearly explains the tool lists curated collections (named cross-org playlists), distinguishes from fixed taxonomy, and provides a concrete example. Differentiates from siblings like get_collection.

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?

Explicitly guides when to use get_collection (full member list) and get_collection_releases (release feed) instead, plus describes pagination 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions listing and optional filtering but fails to describe critical behaviors such as pagination, rate limits, authentication requirements, or what 'indexed' means operationally. This leaves significant gaps for a tool that likely returns multiple items.

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 extremely concise—a single sentence that efficiently conveys the core functionality without unnecessary words. It is front-loaded with the main action and resource, making it easy to parse quickly.

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 annotations and output schema, the description is incomplete. It doesn't explain what 'indexed' entails, how results are structured, or any behavioral constraints. For a list operation with potential complexity, more context is needed to guide the agent effectively.

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 already documents both parameters ('query' and 'platform') with clear descriptions. The description adds no additional semantic context beyond mentioning optional filtering, which is implied by the schema. This meets the baseline for high schema coverage.

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 with a specific verb ('List') and resource ('all indexed organizations'), and mentions optional filtering. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_organization' or 'search_registry', which prevents a perfect score.

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 mentions optional filtering but doesn't specify scenarios where this tool is preferred over siblings like 'search_registry' or 'get_organization', leaving the agent without usage context.

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 full burden. It implies this is a read operation ('List') but doesn't explicitly state safety aspects like read-only nature, potential rate limits, or authentication needs. The description adds useful context about product hierarchy and scoping, but lacks behavioral details like pagination or error handling.

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?

Three sentences with zero waste: first defines what products are with examples, second explains the parameter usage clearly, third provides filtering guidance. Every sentence adds essential information, and the structure is front-loaded with the core purpose.

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 tool with one optional parameter and no output schema, the description is quite complete. It explains the resource, provides usage guidance, and distinguishes from siblings. The main gap is lack of output format details, but given the tool's simplicity and good parameter coverage, this is minor.

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 already documents the single parameter. The description adds value by explaining when to use the parameter ('Pass an organization filter to scope to one org; omit it to see every indexed product') and providing context about what products represent, but doesn't add syntax or format details beyond what the schema provides.

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 verb 'List' and resource 'products', specifies they are 'optional grouping layer between organizations and sources', and provides concrete examples (Vercel → Next.js, Turborepo). This distinguishes it from siblings like list_organizations and list_sources by explaining the hierarchical relationship.

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?

Explicit guidance is provided: 'Pass an organization filter to scope to one org; omit it to see every indexed product.' This tells the agent when to use the parameter versus when to omit it, and the context of 'multi-product orgs' helps differentiate from sibling tools that handle organizations or sources directly.

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 full burden for behavioral disclosure. It states this is a list operation but doesn't describe what 'indexed' means, whether results are paginated, what format they return, or any rate limits or permissions required. This leaves significant gaps for an agent to understand how to use it effectively.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple list operation and front-loads the essential 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?

Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'indexed' means, what data is returned, or how results are structured. For a tool with 1 parameter and multiple sibling alternatives, more context is needed for effective use.

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 already documents the single optional 'organization' parameter with its purpose. The description doesn't add any parameter-specific information beyond what's in the schema, meeting the baseline for high coverage.

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 ('List all') and resource ('indexed changelog sources'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get_source' or 'list_organizations', which prevents a perfect score.

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 like 'get_source' (for single source details) or 'list_organizations' (for organizational listings). There's no mention of prerequisites, exclusions, or contextual usage scenarios.

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. The description adds that it does not probe the domain or materialize anything, and surfaces a 'no match' message for unknown domains, providing behavioral context beyond annotations.

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?

Three well-structured sentences: purpose, normalization details with example, and output/alternatives. No redundant information, each sentence 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?

For a simple one-parameter tool with annotations and no output schema, the description covers transformation, output content, and alternative guidance comprehensively, leaving no significant 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?

The input schema covers the parameter with a description, and the description adds detailed normalization steps (stripping scheme, www, path, trailing slash, lowercasing) with examples, enhancing understanding 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 clearly states the tool resolves a domain to the owning org or product, specifies the input (domain) and output, and distinguishes it from the sibling tool get_organization by noting when to use each.

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?

Explicitly tells when to use lookup_domain (URL-shaped input) versus get_organization (slug or id), and clarifies that it's a pure resolution without probing, giving clear context for 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and the description complements by detailing behavior like silent degradation from hybrid to lexical when vector infra is unavailable, and the flagging of results. No contradictions.

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?

Description is three paragraphs long and front-loads core purpose, then provides usage examples. Slightly longer than minimal but each sentence earns its place; could condense a bit without loss.

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 7 parameters (1 required), no output schema, and no nested objects, the description comprehensively explains the search behavior, fallback mechanisms, and scoping options. Leaves little ambiguity for the agent.

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 baseline is 3. Description adds value for type and entity/org parameters with concrete usage examples, though it doesn't deeply explain mode or include_coverage beyond what's in 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 clearly states 'unified search across the registry and release content' and enumerates the three result sections, making the tool's purpose distinct from siblings like search_registry and search_releases which are narrower.

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?

Provides explicit when-to-use guidance: e.g., use type: ['catalog'] for fast registry-only lookups, type: ['releases'] for release-only search, and omit type for full search. Also explains how to scope with entity and organization.

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 effectively describes the semantic search behavior, fallback to lexical search when Vectorize is unavailable, and the types of entities returned (orgs, products, sources). However, it doesn't mention performance characteristics like rate limits, authentication needs, or error handling, leaving some gaps.

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 front-loaded with the core purpose, followed by usage context and fallback behavior. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 search tool with no annotations and no output schema, the description does well by explaining the semantic behavior, fallback, and entity types. However, it lacks details on output format (e.g., structure of returned results) and error conditions, which could be important given the tool's complexity and lack of output 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds value by explaining that the query is 'natural-language' and for semantic matching, and implies the 'kind' parameter filters entity types. However, it doesn't provide additional syntax or format details beyond what the schema offers, meeting the baseline for high coverage.

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 performs 'semantic search across the registry' and specifies it 'returns orgs, products, or sources that match the query by meaning, not just keyword.' It distinguishes itself from keyword-based search and from sibling tools like get_organization or list_organizations by emphasizing semantic matching and fallback behavior.

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 when to use this tool: 'Useful when you know what kind of thing you're looking for ('observability vendor with open-source agent') but not its exact name.' It also mentions the fallback to lexical search, providing context on limitations. This gives clear guidance versus using exact-match sibling tools like get_organization.

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 full burden and delivers excellent behavioral disclosure. It explains the hybrid retrieval approach (FTS5 + semantic vectors fused via RRF), result discrimination capability (kind field for release vs changelog_chunk), and important fallback behavior (silent degradation to lexical FTS when vector infrastructure is unavailable). This goes well beyond what the input schema provides.

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 efficiently structured in three sentences with zero waste. The first sentence establishes core functionality, the second explains result discrimination, and the third covers important fallback behavior. Every sentence earns its place by adding distinct 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?

For a search tool with 6 parameters and no output schema, the description provides excellent behavioral context about retrieval methods and fallback behavior. The main gap is lack of information about return format or result structure, which would be helpful since there's no output schema. However, the description of result discrimination (kind field) partially addresses this.

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 already documents all 6 parameters thoroughly. The description doesn't add specific parameter semantics beyond what's in the schema descriptions, though it does provide context about the overall retrieval strategy that informs parameter usage. Baseline 3 is appropriate when 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 clearly states the tool searches indexed release notes using hybrid retrieval (FTS5 + semantic vectors) with fallback to lexical FTS when needed. It distinguishes from siblings like get_release (single release) and get_latest_releases (non-search) by specifying search functionality with result discrimination between 'release' and 'changelog_chunk' hits.

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 about when to use this tool (searching release notes with hybrid retrieval) and mentions fallback behavior when vector infrastructure is unavailable. However, it doesn't explicitly contrast with alternatives like search_registry or provide specific 'when-not-to-use' guidance beyond the implied scope of release notes.

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