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Agent.ai MCP Server

by OnStartups

get_data_from_user_uploaded_files

Search user-uploaded files using semantic queries to find relevant information. Configure search parameters like document count and relevance threshold for targeted retrieval.

Instructions

Retrieve semantic search results from user-uploaded files for targeted information retrieval.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesEnter the search query to find relevant information from uploaded files, such as 'project summary' or 'budget report'.{{user_input}}
assistant_filesYesSpecify which uploaded files to search in, such as 'recent_uploads' or 'project_documents'.{{assistant_files}}
max_documents_countYesSet the maximum number of document chunks to retrieve, like '5' or '10'.10
score_cutoffYesAdjust the score threshold for search relevance, like '0.2' for more general results or '0.5' for more specific results.0.2
output_variable_nameYesProvide a variable name to store the results, such as 'file_search_results' or 'upload_data'.knowledge_base_results
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It states 'retrieve semantic search results' but does not specify that the operation is read-only, how results are scored or chunked, or any authentication/rate limit requirements. The description adds minimal behavioral context beyond the generic action.

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

Conciseness3/5

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

The description is a single sentence of 10 words, which is concise but lacks necessary detail for a tool with 5 required parameters. While front-loaded, it is underspecified and could benefit from additional sentences to cover key aspects like return value or filtering behavior.

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?

Given the tool has 5 required parameters, no output schema, no annotations, and operates on user-uploaded files, the description is incomplete. It does not explain the format of results, how files are accessed, or any constraints. The description fails to provide sufficient context for an agent to use the tool 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 100%, so the input schema already documents all 5 parameters adequately. The tool description adds no additional meaning or context beyond what is in the schema, meeting the baseline for parameter semantics.

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?

The description clearly states the tool retrieves semantic search results from user-uploaded files, using a specific verb and resource. However, it does not differentiate from the sibling tool 'get_data_from_builder_knowledge_base', which likely performs similar retrieval from a different source.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives, such as other search or retrieval tools in the sibling list. There are no explicit exclusions, prerequisites, or usage examples beyond what is implied by the name.

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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