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blackboard_search

Search a shared blackboard for entries relevant to a natural-language query, returning the top-K ranked matches to reduce noise in your context window.

Instructions

Search the shared blackboard for entries relevant to a query and return the top-K ranked matches. Use this instead of blackboard_list when you need relevant keys rather than all keys — it keeps noise out of your context window. Read-only. Ranks semantically when the server has an embedding provider wired, otherwise by deterministic lexical overlap (response includes which mode was used). Returns {ok:true, mode, results:[{key, score, snippet, sourceAgent}], count}. Follow up with blackboard_read for the full value of a specific key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language search query (e.g. "budget decisions for Q3")
top_kNoMaximum number of results to return (default 5, max 50)
agent_idYesThe agent performing the search (used for scoped access checks)
min_scoreNoMinimum relevance score 0-1 (default 0)
Behavior5/5

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

No annotations provided, but description discloses read-only nature, semantic vs lexical ranking, return format, and hints to follow up with blackboard_read.

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?

Concise, front-loaded with main action, then usage guidance and behavioral details. Each sentence adds value.

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 lack of annotations and output schema, description covers purpose, usage, ranking behavior, return format, and follow-up, making it complete for a tool with 4 parameters.

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 coverage is 100% with good descriptions for all parameters. Description adds context about return format but doesn't significantly enhance parameter understanding beyond schema.

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?

States 'Search the shared blackboard for entries relevant to a query and return the top-K ranked matches', clearly specifying verb, resource, and outcome. Also distinguishes from sibling blackboard_list.

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?

Explicitly says 'Use this instead of blackboard_list when you need *relevant* keys rather than *all* keys'.

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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