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find_related

Find semantically related chat history from any free-form text such as paragraphs, code, or logs. Retrieve prior conversations covering similar topics without crafting keyword queries.

Instructions

Find chats related to a free-form context (not to a short query).

Different from search_chats: this tool accepts long input (a paragraph, file contents, what is being discussed right now) and returns semantically related chats. Internally uses pure vector search (no FTS) because for long inputs exact words weigh less than embedding similarity.

USE when:

  • You want "more like this": you have text in hand and want to see which history chats covered similar topics.

  • The user is pasting a paragraph / code / log and you need prior context automatically, without having to craft a keyword query.

  • You want to proactively suggest relevant chats without the user asking for them.

For short-keyword searches, use search_chats. For a recent chronological list, list_recent_chats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoNoOptional. Same repo boost as `search_chats`.
limitNoNumber of results (default 5, max 50).
contextYesFree-form text. Truncated to 4000 chars before embedding to bound latency.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so the description bears full responsibility. It explains the internal mechanism (pure vector search, no FTS), the truncation of context to 4000 chars, and the differentiator from keyword search. The read-only nature is implied by 'find' and the context of retrieving related chats. This disclosure is complete for the tool's behavior.

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 composed of a short introductory sentence, an explanation of internals, and three bullet points for usage. Every sentence serves a purpose, no fluff. The structure is well-organized with clear headings and bullet points, making it easy to scan.

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?

Given the tool's complexity (3 parameters, vector search, output schema present), the description covers purpose, usage, behavioral details, and parameter semantics thoroughly. The existence of an output schema reduces the need to describe return values. The description is complete for an agent to decide when and how to use this tool.

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

Parameters4/5

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 adds value by explaining the truncation of 'context' to 4000 chars and noting that 'repo' has the same boost effect as in search_chats. It also clarifies that 'limit' defaults to 5 and max is 50, which is already in the schema but reaffirmed.

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 begins with 'Find chats related to a free-form context (not to a short query).' This clearly states the verb (find), the resource (chats), and the specific input type (free-form context). It distinguishes from search_chats by noting it accepts long input and uses vector search, making the purpose unambiguous.

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 description provides three explicit 'USE when' scenarios (more like this, pasting long text, proactive suggestions) and two alternatives for other cases (short-keyword -> search_chats, recent chronological -> list_recent_chats). This gives clear guidance on when to use this tool versus its siblings.

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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