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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds useful context beyond that — the FTS5 engine, the fixed corpus size, and the sub-millisecond latency — but says nothing about ordering, whether limit is capped, or what a hit looks like, which matters for a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no filler; the scale and domain of the corpus are front-loaded and every phrase carries information. Nothing needs to be trimmed.

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 (two params, 100% schema coverage, annotations present), so most of the burden is already met. However, with no output schema the description should describe the shape of a result record or at least the ranking/ordering, and it does not.

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 both query and limit are already documented with an FTS5 example and a default. The description adds no additional meaning about query syntax, tokenization behavior, or how limit interacts with ranking, 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 gives a specific verb ('search') and a specific corpus ('17,671 Google Cloud, Vertex AI, and A2A service records'), so an agent can tell this is a catalog lookup tool rather than a CVE audit or package health check. It stops short of naming or contrasting the closest sibling, vault_knowledge_search, which is also a search tool.

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

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

Usage is only implied by the corpus scope: use it when you need to find a Google Cloud / Vertex AI / A2A service entry. There is no explicit statement of when to prefer this over vault_knowledge_search, nor any exclusion or prerequisite guidance.

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, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description does add that the call fans out across two registries in parallel, which tells the agent to expect aggregated cross-ecosystem results rather than a single-registry lookup. It says nothing about partial failures (e.g., package missing on PyPI) or rate-limit behavior for an open-world remote call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single ten-word sentence with no filler and the ecosystem scope front-loaded. It is efficient, though efficiency here reflects under-specification rather than tight editing.

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 is the only place the agent could learn what a "pulse" result contains, and it does not say. For an open-world tool hitting two external registries, the missing return-shape and partial-failure behavior leave a real gap despite the simple one-parameter surface.

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% for the single required parameter, so "Target package name" is already documented structurally. The description adds no meaning about naming conventions – scoped NPM names like @scope/pkg versus PyPI normalization – which is the one piece of semantics an agent actually needs here. Baseline 3 applies when the schema carries the weight.

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?

"Parallel health pulse inspection across NPM and PyPI ecosystems" names the two registries it queries and implies a read-only diagnostic, but "health pulse inspection" is undefined jargon – an agent cannot tell whether this returns downloads, version freshness, deprecation status, or a composite score. It also does no work to distinguish itself from security_cve_audit, which plausibly overlaps on package risk.

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?

There is no statement of when to call this versus the sibling tools (security_cve_audit in particular), no prerequisites, and no indication of what to do when a package name exists on one registry but not the other. The word "parallel" hints that both ecosystems are checked in one call, but that is inference, not guidance.

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, openWorldHint=true, and destructiveHint=false, so the safety profile is fully covered elsewhere. The description's only additive context is naming OSV.dev as the external source, which reinforces the open-world nature of the query. It says nothing about whether a version is needed for meaningful results or how advisories are ranked/returned, leaving gaps for a lookup tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler or redundancy. It is efficient, though the terseness borders on under-specification for a tool whose siblings need disambiguating.

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 three-parameter read-only lookup with annotations and full schema coverage, the description is minimally sufficient to pick and invoke the tool. It stops short of covering result expectations (severity, advisory count) and the behavioral effect of the optional version parameter, which is the one thing an agent would most want clarified.

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 all three parameters (package, version, ecosystem with PyPI default) are already documented in the schema. The description adds no meaning beyond that—it does not say that omitting version returns all known advisories or how the ecosystem string should be matched.

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

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

States a specific verb ("Audit") and resource ("package vulnerabilities and CVE advisories") plus the underlying data source (OSV.dev), so the agent knows exactly what the call returns in kind. It does not distinguish itself from the nearest sibling, package_health_pulse, which an agent could plausibly confuse with this vulnerability lookup.

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

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

The description gives no when-to-use guidance, no prerequisites, and no exclusions relative to package_health_pulse, gcp_catalog_search, or vault_knowledge_search. The agent must infer from the word "CVE" alone when this audit is the right choice.

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, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds useful context: the FTS5 engine (implying query syntax and ranking behavior) and the corpus scale (36,628 chunks, 106,308 triples). It does not disclose result ordering, pagination, or whether both corpora are searched or merged in one call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with zero filler that surfaces the search engine and corpus targets immediately. The exact record counts are borderline noise, but they do convey scale, so little needs trimming.

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 two-parameter search tool with annotations present and no output schema, the definition covers what is searched but not how results are returned (fields, ranking, snippets, pagination via limit). An agent can invoke it, but cannot predict the response shape or how the two corpora are combined in results.

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 both parameters are already documented, making 3 the baseline. The description adds the FTS5 detail, which hints that the query accepts full-text syntax, but it gives no syntax examples or clarifications, and notably does not resolve the tension that the schema describes query as being for the 'creator knowledge graph' 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 names a specific mechanism (FTS5 search) and the exact corpora it queries (video transcript chunks, knowledge graph triples in vault.db), so an agent knows precisely what it retrieves. Sibling tools (gcp_catalog_search, package_health_pulse, security_cve_audit) occupy entirely different domains, so implicit differentiation is sufficient, though no sibling is named explicitly.

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?

There is no statement of when to use this tool, when not to, or what alternative to pick. The corpus inventory implies it is for vault knowledge lookups, but no conditions, prerequisites, or exclusions are given, so an agent must infer selection criteria.

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