Fillin

query_cves

Read-onlyIdempotent

Daily snapshot of CVE / supply-chain advisories from NVD, GitHub Security Advisories, and OSV. Use before merging dependency updates, when triaging an alert, or when a user asks "is package X compromised".

Each result row carries a structured `affected` list (one entry per
affected package: ecosystem, name, vulnerable_range, patched_range) and
a numeric `severity_score` (CVSS baseScore, nullable on OSV-only rows).
A buyer can act on the returned row — pin to `patched_range` — without
a second hop to NVD or GHSA.

Input Schema

TableJSON Schema

Name	Required	Description
`k`	No	1-20
`query`	Yes	Vulnerability / supply-chain query.
`cutoff`	Yes	Training cutoff as ISO-8601 date.
`min_severity`	No	Optional CVSS baseScore floor (0.0-10.0). When set, rows with a populated severity_score below this value are dropped, and rows whose severity is unknown are skipped. Use 7.0 for high+critical only, 9.0 for critical only.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context: it is a daily snapshot (freshness), rows carry structured affected list and nullable severity_score, and results are directly actionable without an external hop. This goes beyond minimal 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.

Conciseness5/5

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

The description is efficiently structured: the first paragraph front-loads the core purpose and use cases, the second adds concise output semantics. Every sentence contributes, with no redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and annotations covering safety, the description is complete enough: it specifies data sources, intended use cases, and the actionable nature of results. It gives an agent sufficient context 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.

Parameters3/5

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

The input schema covers all four parameters with detailed descriptions (k, query, cutoff, min_severity). The description adds no parameter-specific information beyond what the schema provides, so the baseline of 3 applies.

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

Purpose5/5

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

The description clearly states the tool returns CVE and supply-chain advisories from NVD, GitHub Security Advisories, and OSV, which is a specific verb+resource. It distinguishes itself from sibling query tools (query_markets, query_papers) by explicitly focusing on vulnerability/supply-chain domain.

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

Usage Guidelines4/5

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

The description provides strong when-to-use context ('before merging dependency updates, when triaging an alert, or when a user asks...'). It does not explicitly name alternative tools or state when not to use it, so it lacks the full explicit contrast expected for a 5.

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

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TDQS

A3.9/5.0

Disambiguation2/5

Several tools overlap significantly: fillin_query, glyph_search, and retrieve_auto all perform post-cutoff retrieval and differ only in output substrate, and fillin_health and fillin_stats both report corpus stats. This creates ambiguity for agents choosing between them.

Naming Consistency3/5

Tool names mix three conventions: fillin_* (fillin_query, fillin_mint), query_* (query_cves, query_papers), and bare names (encode, glyph_search, retrieve_auto). While readable and mostly snake_case, the lack of a uniform prefix or verb pattern makes naming inconsistent.

Tool Count4/5

14 tools is within the acceptable range for a multi-feature server, but there is some redundancy (two health/stats tools, three retrieval variants), making the count feel slightly inflated.

Completeness5/5

The server covers retrieval (text/glyph/auto), encoding, marketplace operations (mint, search, buy), and domain-specific queries (CVEs, frontier AI, markets, papers), with no obvious missing capabilities.

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