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search_knowledge

Search historical lessons, decisions, and playbooks using keywords. Retrieve and filter past knowledge to inform current tasks.

Instructions

搜索知识库(lessons/decisions/playbooks)。 / Search lessons, decisions, and playbooks by keyword.

**Lifecycle: retrieval** — 在对话中需要检索历史知识时调用。
Lifecycle: retrieval — call during conversation when past knowledge is needed.

Call when the user asks to find knowledge about a specific topic,
or recalls a procedure ('X how to' / 'X steps').

If you only have a project path and no query, use get_relevant_knowledge;
if you have an existing knowledge ID, use explore_knowledge(mode="similar").

Args:
    query: Search query keywords.
    scope: Search scope: 'all', 'lessons', 'decisions', or 'playbooks'.
    limit: Maximum number of items to return (default 10).
    filters_json: Optional JSON string with filter criteria. Supported keys:
        - "domain": str — only items whose domain contains this value
        - "tier": str — only items matching this tier ('staging' or 'verified')
        - "date_after": str — ISO date string, only items created after this date
        Example: '{"tier": "verified", "domain": "python"}'
    include_freshness: Attach a per-item freshness hint (fresh/aging/stale)
        to each returned item. Default False keeps the response unchanged.
    max_field_chars: Per-field size cap. Every string field in each returned
        item (detail/reasoning/description/steps/...) longer than this is
        clipped with a "[+N chars truncated]" marker so a few large bodies
        cannot blow up the client. Item shape, ids, and headlines are kept.
        Set 0 for full untruncated bodies (default 400).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
scopeNoall
filters_jsonNo
project_folderNo
max_field_charsNo
include_freshnessNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It discloses the lifecycle as retrieval (read-only), explains parameter effects (e.g., truncation with max_field_chars, freshness hints), and implies non-destructive behavior. It does not explicitly state auth or rate limits, but for a search tool the transparency is good.

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

Conciseness4/5

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

The description is front-loaded with a summary and lifecycle, then usage guidance, then parameter details. It is well-structured but somewhat verbose due to bilingual text. Every sentence is useful, but conciseness could be slightly improved.

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

Completeness4/5

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

Given the tool's complexity (7 parameters, no annotations, output schema exists), the description covers key behaviors: parameter explanations, truncation, freshness hints. It lacks error handling details but is sufficient for an AI agent to use 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?

Schema description coverage is 0%, so the description must explain parameters. It does so for most (query, scope, limit, filters_json with example, include_freshness, max_field_chars) but omits project_folder, leaving a gap. The explanations add value beyond the schema (e.g., filters_json keys).

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 searches a knowledge base of lessons, decisions, and playbooks by keyword. It distinguishes from siblings like get_relevant_knowledge (for project path without query) and explore_knowledge (for existing ID), 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 explicitly says when to call this tool (when user asks to find knowledge or recalls a procedure) and when not to, providing alternative tools (get_relevant_knowledge, explore_knowledge). This gives clear usage guidance.

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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