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Agent.ai MCP Server

by OnStartups

get_data_from_builder_knowledge_base

Fetch semantic search results from a builder's knowledge base to retrieve relevant structured data for analysis. Filter by score and document count for precise results.

Instructions

Fetch semantic search results from the builder's knowledge base for data analysis. This would allow your AI actions to leverage relevant structured data from the knowledge base based on filtering criteria.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesEnter the search query to retrieve relevant knowledge base entries, such as 'best sales strategies'.{{user_input}}
builder_knowledge_bases_listYesSelect which knowledge base to use for the search. For example, choose 'Product Docs' or 'Marketing Tips'.
max_documents_countYesSpecify the maximum number of document chunks to return, such as '5' or '10'.10
score_cutoffYesSet the score threshold for search relevance, such as '0.2' for broader results or '0.7' for more precise matches.0.2
output_variable_nameYesAssign a variable name to store the knowledge base results, like 'kb_results' or 'search_output'.knowledge_base_results
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only states 'fetch', implying read-only, but does not confirm no side effects, auth requirements, or rate limits. Minimal behavioral insight beyond basic function.

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?

Two sentences with no wasted words. Front-loaded with the core action and benefit. Efficient and to the point.

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

Completeness2/5

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

No output schema exists, but the description does not explain return values, result format, pagination, or error handling. Parameters like max_documents_count hint at limits but are not elaborated. The description leaves an agent uncertain about what to expect from the results.

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 parameter descriptions explaining each field. The description adds little extra meaning beyond 'filtering criteria'. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it fetches semantic search results from a builder's knowledge base. The verb 'fetch' and resource 'builder's knowledge base' are specific. However, it does not distinguish from sibling tools like 'query_agent_kb' or 'get_data_from_user_uploaded_files', leaving ambiguity about what 'builder' refers to.

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

Usage Guidelines3/5

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

The description mentions 'for data analysis', giving a hint about when to use, but no explicit guidance on when not to use or alternative tools. The context is implied, not explicit.

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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