Fillin

fillin_query

Read-onlyIdempotent

Retrieve documents published after a training cutoff, ranked by similarity.

Call this whenever the user asks about events, releases, papers, issues,
or news that might post-date your training data. Fillin only returns
documents published AFTER `cutoff`, so nothing returned is redundant
with what the model already knows.

Args:
    query: Natural-language search query (e.g. "rust async runtimes").
           Max 512 characters.
    cutoff: ISO-8601 date representing the agent's training cutoff
            (e.g. "2026-01-01"). Documents on or before this date are
            excluded from results.
    k: Number of documents to retrieve, 1-20. Defaults to 5.

Returns:
    A dict with:
      - cutoff: echoed cutoff (ISO timestamp)
      - query: echoed query
      - gap_days: days between cutoff and now
      - results: list of {id, source, url, published_at, title, text, score}

Input Schema

TableJSON Schema

Name	Required	Description
`k`	No	Number of documents to retrieve (1-20).
`query`	Yes	Natural-language search query, max 512 chars.
`cutoff`	Yes	Training cutoff as ISO-8601 date (e.g. 2026-01-01). Documents on or before this date are excluded.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior5/5

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

The annotations already declare readOnlyHint, openWorldHint, and idempotentHint, but the description adds substantial behavioral detail: documents on/before cutoff are excluded, results are ranked by similarity, and the return structure includes echoed fields and gap_days. It fully discloses the tool's side-effect-free, retrieval-only nature and the exact output envelope.

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: a one-sentence purpose, a use-case trigger sentence, then clearly labeled Args and Returns sections. Every line adds value, there is no fluff, and the format makes it easy for an agent to parse the key information quickly.

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?

For a three-parameter, read-only retrieval tool, this description is complete. It covers purpose, trigger conditions, parameter semantics, and the exact return shape (including nested fields) even before the output schema is considered. The context signals (read-only, idempotent, open-world) are consistent with the described behavior.

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 schema covers 100% of parameters with descriptions, so the baseline is 3. The description largely paraphrases schema information (e.g., max length, default k, cutoff exclusion), but it does add a helpful example for query and contextualizes cutoff as 'the agent's training cutoff.' This is marginal added meaning, not enough to push beyond the coverage baseline.

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 opens with a clear, specific statement: 'Retrieve documents published after a training cutoff, ranked by similarity.' It names the verb (retrieve), the resource (post-cutoff documents), and the unique ranking behavior. It further differentiates from typical search by emphasizing the post-cutoff constraint, even though sibling tools are not named explicitly.

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 gives explicit when-to-use guidance: 'Call this whenever the user asks about events, releases, papers, issues, or news that might post-date your training data.' It also explains the benefit over the model's internal knowledge. However, it does not explicitly mention when not to use the tool or name alternative sibling tools, so it stops short of 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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