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Search local cache (FTS5)

cache_search
Read-onlyIdempotent

Search through locally cached pages to recall previously fetched content by keyword, reducing the need to re-fetch from the web.

Instructions

Full-text search over pages already fetched into the local SQLite FTS5 index.

Best for:
- Recalling something the user/agent fetched earlier in the conversation
  ("what did that Wikipedia page say about X").
- Avoiding re-fetching content already in the local cache.
- Quick keyword grep across the corpus you've built up.

Not recommended for:
- Discovering new pages on the open web -> use `search` or `research`.
- When the cache is empty (fresh install) -> `search`/`research` first to
  populate it.

Returns:
- markdown (default): a per-hit list of title, URL, and a `[bracket]`-
  highlighted snippet around the matched terms.
- json: list of {url, title, snippet, author, date, sitename}. The last
  three are "" when the cached row predates metadata capture.

Common mistakes:
- Treating this like web search — it ONLY hits pages already in the local
  cache. If the user hasn't fetched anything, you'll get zero hits.
- Using natural-language phrases without quoting them; FTS5 splits on
  whitespace as AND. For an exact phrase use `"like this"`.

Args:
    query: FTS5 query. Bare terms = AND. Supports OR / NOT, prefix
        (`term*`), and phrase (`"exact phrase"`).
    limit: Max hits to return.
    format: "markdown" or "json".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint=false, and idempotentHint, but the description adds substantial context: it explains cache-only behavior, FTS5 query semantics, default format, snippet highlighting, empty cache behavior, and empty metadata fields for old rows. No contradiction with annotations.

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 well-structured with clear section headers (Best for, Not recommended for, Returns, Common mistakes, Args). It is longer than average but every sentence contributes functional guidance; no filler or repetition.

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?

Despite an output schema, the description explains return value details (per-hit list, bracket highlighting, JSON fields, empty strings for old data). It also covers edge cases like empty cache and common usage mistakes, making it fully contextual for an AI agent.

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

Parameters5/5

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

Schema coverage is 0%, but the description explains all three parameters clearly: query syntax (AND, OR, NOT, prefix, phrase), limit as max hits, and format with 'markdown' or 'json' values and their output differences. This fully compensates for the lack of schema descriptions.

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 specific verb and resource: 'Full-text search over pages already fetched into the local SQLite FTS5 index.' It clearly distinguishes the tool from siblings like 'search' and 'research' by emphasizing 'local cache only' and explicitly contrasting with web search.

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

Usage Guidelines5/5

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

The 'Best for' and 'Not recommended for' sections provide explicit usage criteria, including when to use alternatives ('use `search` or `research`'). This directly answers when to use vs. avoid the tool.

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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