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

Without annotations, the description carries the full burden. It details return types (license, supply-chain risk, popularity, repo signals) and explains caching vs live-probing. However, it omits potential rate limits or side effects from GitHub API calls.

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 4 concise sentences with front-loaded purpose. Every sentence adds value: action+results, caching detail, and use cases. 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?

Given the presence of an output schema and single parameter, the description covers core behavior, caching, and usage cases. Missing details on rate limits or authentication, but overall sufficient for an agent to select and invoke the tool 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?

Despite 100% schema coverage, the description adds significant meaning: it clarifies that the tool works on any external repo (not just catalog) and explains behavioral differences based on catalog membership, which the schema's regex and description do not convey.

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: auditing any external GitHub repo for license, supply-chain risk, popularity, and repo signals. It distinguishes itself from siblings by emphasizing external repos and one-shot comprehensive audit.

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 use cases like 'is github.com/X/Y safe to bundle?' and 'what license is github.com/X/Y under?'. It also explains caching vs live-probing behavior, but does not explicitly mention when to use alternatives like buygit_deep_audit.

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. It discloses the output format (compatible/review/incompatible with plain-English note) and a disclaimer. However, it does not explicitly state read-only nature, side effects, or other behavioral traits, which are important for an agent to understand.

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 front-loaded: first sentence states purpose, second describes output, third adds uniqueness and a hint. Every sentence adds value with 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?

Given the simplicity of the tool (two string inputs) and the existence of an output schema, the description covers the key aspects: purpose, input semantics, output format, and a legal disclaimer. It could mention any edge cases or dependencies, but for typical use it is sufficient.

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. The description does not add meaning beyond the schema; it reiterates that inputs are SPDX IDs but does not provide additional context or constraints beyond what the schema already covers.

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: checking SPDX license compatibility between two licenses. It specifies the return values (compatible/review/incompatible) and uses an example question to illustrate usage. The mention of being the only MCP without a separate SCA tool distinguishes it from external tools, and sibling tools don't overlap directly.

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 usage example and a disclaimer ('not legal advice'), but does not explicitly state when to use this tool vs alternatives or when not to use it. It implies it is the go-to tool for license compatibility among MCPs but lacks direct comparison with 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?

With no annotations provided, the description must disclose behavioral traits. It reveals graceful handling of unknown slugs (non-error 'not found' entries) and the range of listings compared. It does not mention safety or idempotency, but the read-only nature is implied.

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, both front-loaded with critical information: first sentence covers purpose and output fields, second covers usage and error handling. No unnecessary 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 presence of an output schema, the description does not need to detail return values. It covers purpose, usage, error behavior, and differentiation from siblings, making it complete for a comparison tool with one parameter.

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 only parameter 'slugs' has no schema description (0% coverage). The description adds context by linking slugs to prior tool results and specifying error behavior, which clarifies the parameter's meaning and expected format.

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 explicitly states 'Single-call side-by-side of 2-5 listings' with specific fields compared, clearly defining the verb (compare), resource (listings), and scope. It distinguishes from sibling tools by noting that equivalent workflows require 4+ calls and lack license compatibility 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 instructs to pass slugs from prior tool results and explains that unknown slugs return 'not found' entries instead of errors. It contrasts with alternative workflows, but does not explicitly specify when not to use this 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?

No annotations provided, so description must cover behavior. It discloses that companion MCPs are spawned in parallel, soft-fails per companion, and always returns something agent-actionable. It also explains that missing companions surface as federation_failures with hints. Could mention auth or rate limits but is sufficient.

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 tight paragraph of four sentences, front-loaded with the core purpose. Every sentence adds value, with 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?

Given the tool's complexity and that an output schema exists, the description covers the main aspects: federated execution, handling of missing companions, and guaranteed actionable output. It could specify the output structure but is acceptable.

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 good parameter descriptions. The description does not add significant additional meaning beyond the schema for parameters. 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 the tool does a federated audit across Socket, OpenSSF, and TruffleHog in parallel, combining their findings with catalog signals. It distinguishes from siblings by emphasizing it is the only MCP that one-shots a multi-vendor supply-chain check, avoiding separate installations.

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 tells when to use this tool (for a combined multi-vendor check) and contrasts with alternatives (installing and querying four separate MCPs). It does not explicitly list when not to use it, but the context is clear enough for an 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 provided, the description fully discloses behavior: it returns snapshot-driven deltas when conditions are met, otherwise returns current state with a 'gated_on' hint. This informs the agent about the tool's limitations and expected outputs.

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 concise sentences, front-loading the main purpose and then detailing the conditional behavior. No extraneous words; every sentence provides 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 the tool's complexity (parameter count 3, output schema present), the description covers all critical aspects: the diff operation, conditional behavior based on operator settings, and a hint mechanism. It fully equips an agent to decide when and how to invoke the tool.

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?

All three parameters are described in the input schema with 100% coverage. The description adds value by explaining that 'slug' comes from a prior tool call, 'from' is clamped to listing.created_at, and 'to' defaults to now, which goes beyond the schema's basic 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 clearly states it performs a time-window diff for a BuyGit listing, checking changes in license, popularity, or risk between two dates. It explicitly claims to answer questions no other MCP can, distinguishing it from siblings.

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 the tool (to detect regressions over time) and provides clear conditions (requires TRENDING_V2_ENABLED and snapshot table; otherwise returns current state with a hint). This gives an agent concrete guidance on prerequisites and fallback behavior.

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 the underlying model (Claude Haiku 4.5), the API key requirement, and the error behavior when the key is missing. This adds important behavioral context beyond basic read/write safety. A minor gap is no mention of rate limits or response format details, but the output schema likely covers 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?

Two sentences that are front-loaded with purpose and then provide usage guidance. Every part earns its place; no redundant or vague language.

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 moderate complexity (2 params, 1 required, enums, output schema exists), the description covers purpose, usage sequence, prerequisite (API key), and error handling. The output schema likely documents return values, so that gap is acceptable. A small improvement would be adding a brief note on what each focus option does, but overall 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 description coverage is 0%, so the description must compensate. The description implies the 'slug' parameter (the listing identifier) and the focus areas (license, risk, usage) but does not explicitly describe each parameter or their formats. The focus enum values are hinted but not elaborated. This adds some value but not enough to fully cover the undocumented parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides an 'AI-summarised explanation of a BuyGit listing (license + risk + how to use)', specifying a specific verb, resource, and output coverage. It also distinguishes itself from siblings like buygit_get_listing by offering a digest versus raw fields.

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 says 'Use after buygit_get_listing when the user wants a plain-English digest instead of raw fields', providing clear when-to-use and referencing a specific sibling. It also notes the dependence on ANTHROPIC_API_KEY and the structured error when unset, covering 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 provided, so the description carries full burden. It states the tool returns 'license-compatible, risk-scored alternatives' and mentions available filters, but it does not explicitly confirm read-only behavior, data freshness, or any limitations. For a tool with no annotations, more explicit 'read-only' or 'no side effects' statements would improve 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: three sentences that efficiently convey purpose, distinguishing value, and usage triggers. No redundant words, and the most important information (the tool's unique value) appears first.

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 presence of an output schema (which likely explains return structure), the description provides sufficient context: purpose, comparison to alternatives, and filter parameters. All critical aspects for agent decision-making are covered without needing to add output format details.

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 has 100% coverage with descriptions for all four parameters. The description adds value by explicitly mentioning the 'language' and 'license' filters and giving an example ('e.g. MIT to exclude GPL'), which provides context beyond the schema's generic descriptions. The 'query' parameter is also implied as the library/repo to find alternatives for.

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 action: 'Find license-compatible, risk-scored alternatives to a library or repo.' It differentiates from sibling tools like buygit_search by highlighting that GitHub search lacks license/risk signals, and the tool is distinct from other siblings like buygit_compare (comparison) or buygit_explain (explanation). The verb 'find' and resource 'alternatives' are specific and 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 provides explicit triggers: 'when the user says "what can replace X?", "alternatives to Y", or "the GPL version of Z is blocking me, find an MIT one".' It explains that this tool fills a gap that GitHub search cannot. However, it does not explicitly state when not to use or what alternatives exist (e.g., for general search use buygit_search), but the context is clear enough.

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 burden. It comprehensively discloses included data: secret-scan status, malware flag, upstream health, repo signals, license compatibility warning, pricing, and similar listings. No destructive behavior mentioned; appears read-only. Could add note about safe repetition.

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 no wasted words. First sentence captures purpose and key consolidation benefit. Second sentence lists contents and a critical prerequisite. Highly efficient.

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 moderate complexity, presence of output schema, and comprehensive description covering main outputs and usage prerequisite, the description provides sufficient context for correct agent 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?

Schema coverage is 100%, so baseline 3. Description adds value by clarifying slug must come from a prior search/trending/random/compare result, which is useful context 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 tool retrieves full details for a BuyGit listing, consolidating three separate calls (license, supply-chain risk, popularity). It lists many specific fields and mentions that the slug must come from prior tools like buygit_search, distinguishing it from siblings.

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 the slug must come from a prior tool result, providing clear context for when to use. It implies this is for consolidated view, but does not explicitly exclude cases where deeper audit (buygit_audit_repo) or comparison (buygit_compare) would be more appropriate.

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 provided, so description carries full burden. It discloses that counts are accurate to last crawl (typically <24h), but does not explicitly state read-only behavior or other 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?

Two sentences, first sentence states what it is, second gives purpose and freshness. Front-loaded and no wasteful content.

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 tool with no parameters and an output schema, the description fully explains the data (taxonomy with counts) and freshness. No missing information.

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?

Input schema has 0 parameters, so description need not explain parameters. Baseline 4 applies; description adds value by explaining what the output contains (categories with counts).

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?

Description clearly states it lists the full BuyGit Open Index taxonomy with per-category counts, and explicitly links to using it for finding a category slug for buygit_search. This distinguishes it from sibling tools like buygit_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?

Explicitly says to use this tool to find category slugs for buygit_search or discover catalog content. Does not explicitly state when not to use, but the use case is 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?

With no annotations, the description discloses that the tool returns random listings with specific attributes (license, risk, popularity, pricing). It implies read-only behavior and non-deterministic results. Could mention if any mutation or side effects, but likely none.

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 and output details, followed by use cases and optional parameter 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?

Given the tool's simplicity (2 params, output schema exists), the description adequately covers purpose, parameters, and usage context. No missing critical information.

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% (both parameters described). Description adds context on how parameters are used (count as number of picks, category to narrow pool) but does not provide semantics beyond the schema, so 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 uses clear verb 'Surface' and specifies the resource: random crawler listings with concrete signals (license, risk, popularity, pricing). It clearly distinguishes from siblings like search, trending, or get_listing by focusing on randomness and surprise.

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: 'surprise me', category browsing, seeding suggestions when user has no intent. Does not explicitly state when not to use or mention alternatives, but the use cases are well-defined.

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 provided, so description carries full burden. It discloses that results include license, popularity, risk, and pricing, and mentions sorting options. Does not mention rate limits or edge cases, but for a search tool this is adequate. No contradictions with annotations (none exist).

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 well-structured sentences that front-load the core purpose and key benefits. Every sentence adds value without redundancy. Clear bullet-style listing of filters and sort options.

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 rich input schema (8 params with full descriptions) and presence of output schema, the description provides all necessary context. It covers dataset size, result content, filters, sorting, and when to use vs alternatives. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no extra meaning to parameters beyond what the schema already provides (examples, constraints). Examples in description mirror schema examples, providing no additional value.

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?

Description clearly states it searches curated Git assets, differentiating from raw GitHub search. It lists what results contain (license, popularity, risk, pricing) and available filters, making the tool's purpose distinct from siblings like buygit_trending.

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 prefer this tool: when user wants to use/buy a project, compare alternatives, or check license compatibility. Also explicitly says to use github-mcp for private repos/Issues/commits, providing clear 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?

No annotations provided, but description discloses that the tool returns aggregated, read-only meta information (no destructive actions). Could mention freshness or caching behavior, but still good.

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: first defines what the tool returns, second gives example use cases. No wasted words, front-loaded.

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 tool with an output schema, the description fully covers what the agent needs to know about the tool's purpose and when to use it.

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?

No parameters exist, schema coverage 100%. Description adds context about what the output covers, meeting the baseline of 4 for zero parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool provides meta statistics (total listings, license breakdown, top categories) and is distinct from sibling tools that perform searches or audits.

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 gives use cases: 'how big is the catalog?', 'what license is most common?', 'proving the curated catalog size before recommending it.' Implicitly suggests when NOT to use (for specific repo details, use other 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 provided, so description carries full burden. It describes the output as 'crawler listings' but does not explain behavioral traits like data freshness, caching, rate limits, or error handling for invalid categories. It mentions 'license-aware' but doesn't detail how that affects results. Adequate but not thorough.

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, each adding value. Front-loaded with the key function and output attributes, then use case. No wasted words. Highly efficient.

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 presence of output schema (not shown but indicated), return value explanation not needed. Description covers purpose, use case, key attributes, and filtering. Lacks discussion of error cases or data freshness but is reasonably complete for a trending list tool.

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 description must add meaning. It mentions period ('day|week|month') and category, but not limit. It adds context like 'recent activity' and 'curated, license-aware', but doesn't explain defaults or boundaries. Partial coverage; 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 returns top crawler listings ranked by recent activity with key attributes (license, risk, popularity, pricing). It specifies the time periods and optional category narrowing, and distinguishes from 'GitHub trending noise', implying a curated list. The verb 'list' and resource 'trending' are specific.

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 says 'Use for "what is hot right now in <category>"', providing a clear use case. It implies a curated, license-aware alternative to GitHub trending. However, it does not explicitly state when not to use or reference sibling tools, though the context is reasonably 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 provided, but the description adds value by explaining it's a meta tool and implements MCP Tool Search Tool semantics. Discloses its purpose and function adequately for a search tool with no side effects implied.

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, front-loaded with 'Meta tool,' concise and to the point. Every sentence adds value with 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 meta-tool with one parameter and an output schema, the description is complete. It explains what it does, why it's useful, and how it differs from sibling tools.

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 parameter description 'What the user wants to do, in plain language.' The description's mention of 'plain-English intent' reinforces the schema but does not add new details beyond it.

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?

Description clearly states it's a meta tool that takes plain-English intent and returns ranked tool suggestions. Distinguishes itself from sibling tools by being a meta-tool that selects among them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says it saves the agent from listing every tool description when one tool fits the ask, providing clear usage context. Does not explicitly state when not to use or alternatives, but the context is sufficient.

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