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Algenta MCP

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by thyn-ai
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TypeScript
Hybrid

connect_data

Load data from databases, APIs, or files, select the table or endpoint, and get a reusable dataset ID. If more choices are needed, resume with the connection ID to complete setup.

Instructions

High-level data onboarding flow. Use this instead of advanced connector/source tools for normal users. Connect data once, pick the table/file/endpoint, and get a reusable dataset_id. If the result status is needs_selection, call connect_data again with connection_id and the chosen selection. Creating a connection persists it and the dataset under the active API key's organization, and live sources are dialed during this call; no separate per-route rate limit applies. Returns status with dataset_id and connection_id on success, or status needs_selection with the choices array to pick from.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNoRaw CSV text for direct file_upload datasets.
urlNoURL for direct file_upload or API datasets.
recordsNoInline JSON records for direct file_upload datasets.
json_strNoRaw JSON text for direct file_upload datasets.
providerNoLegacy compatibility field for provider selection. Prefer connector.type plus connector.location/auth/options.
connectorNoCanonical connector envelope with type/location/auth/options. Preferred when the same request shape should work across Python Runtime, TypeScript Runtime, and MCP.
excel_b64NoBase64-encoded Excel payload.
selectionNoLegacy compatibility field for chosen table/query/path. Use the selection object returned in choices when resuming a legacy connection flow.
visibilityNoShared requires admin/owner permissions.
descriptionNo
parquet_b64NoBase64-encoded Parquet payload.
dataset_nameYesName to save and reuse later.
connection_idNoExisting saved connection_id when resuming after selection.
connection_nameNoOptional label for the saved connection.
connection_typeNoLegacy compatibility field. Prefer connector.type with the canonical connector envelope.
connection_configNoLegacy compatibility field for connector credentials/config. Prefer connector.location and connector.auth.credentials.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=false, destructiveHint=false, but the description adds significant behavioral context: 'Creating a connection persists it and the dataset under the active API key's organization, and live sources are dialed during this call; no separate per-route rate limit applies.' This covers persistence, organization scoping, live dialing, and rate limits—valuable beyond annotation flags.

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 front-loaded with purpose, then usage, conditions, side effects, and return format in a logical sequence. Every sentence adds value with zero redundancy. Despite covering multiple aspects, it remains tight and well-organized.

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 (16 params, nested objects, no output schema), the description adequately covers the onboarding flow, return statuses, and resume condition. It doesn't enumerate all input combinations, but the schema fills that gap. Minor omissions like not explaining how to choose among data input formats are acceptable given schema coverage.

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 94%, so parameters are well-documented in the schema. The description adds semantic nuance by distinguishing canonical connector envelope from legacy compatibility fields ('Prefer connector.type plus connector.location/auth/options'), and explaining the selection resume flow. This enriches understanding beyond raw schema definitions.

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?

Description explicitly states 'High-level data onboarding flow' and clearly differentiates from advanced tools: 'Use this instead of advanced connector/source tools for normal users.' It names the specific resource (data) and action (connect/onboard), making it unmistakable among 130+ siblings.

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?

Provides explicit when-to-use guidance ('for normal users' vs advanced tools) and conditional flow: 'If the result status is needs_selection, call connect_data again with connection_id and the chosen selection.' Names the alternative category and gives precise continuation instructions.

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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