Algenta MCP Server

connect_data

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.

Input Schema

TableJSON Schema

Name	Required	Description
`csv`	No	Raw CSV text for direct file_upload datasets.
`url`	No	URL for direct file_upload or API datasets.
`records`	No	Inline JSON records for direct file_upload datasets.
`json_str`	No	Raw JSON text for direct file_upload datasets.
`provider`	No	Legacy compatibility field for provider selection. Prefer connector.type plus connector.location/auth/options.
`connector`	No	Canonical connector envelope with type/location/auth/options. Preferred when the same request shape should work across Python Runtime, TypeScript Runtime, and MCP.
`excel_b64`	No	Base64-encoded Excel payload.
`selection`	No	Legacy compatibility field for chosen table/query/path. Use the selection object returned in choices when resuming a legacy connection flow.
`visibility`	No	Shared requires admin/owner permissions.
`description`	No
`parquet_b64`	No	Base64-encoded Parquet payload.
`dataset_name`	Yes	Name to save and reuse later.
`connection_id`	No	Existing saved connection_id when resuming after selection.
`connection_name`	No	Optional label for the saved connection.
`connection_type`	No	Legacy compatibility field. Prefer connector.type with the canonical connector envelope.
`connection_config`	No	Legacy compatibility field for connector credentials/config. Prefer connector.location and connector.auth.credentials.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedJul 12, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations provided, so the description carries full burden. It explains the high-level flow and a specific status case, but omits any mention of permissions, error handling, destructive behavior, or return format beyond dataset_id. For a tool with 16 parameters, this is insufficient.

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, zero waste, directly front-loads the purpose and key usage instructions. Highly concise.

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 high complexity (16 parameters, nested objects, no output schema), the description is incomplete. It does not explain the overall return structure, error codes, or prerequisites like authentication. The conditional flow is mentioned but not fully detailed.

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 94%, meaning most parameters are already documented in the schema. The description adds context about legacy vs canonical fields and when to use connection_id, but this is minimal extra value. Baseline 3 is appropriate.

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 states a specific verb-resource ('onboarding data flow') and distinguishes it as 'for normal users' instead of advanced tools, but it doesn't name specific sibling tools or clarify the exact difference from similar tools like 'ingest_data' or 'onboard_dataset'.

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 states when to use ('for normal users, instead of advanced connector/source tools') and provides a conditional resumption step: 'If the result status is needs_selection, call connect_data again with connection_id and the chosen selection.' This is clear and actionable.

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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TDQS

C2.7/5.0

Disambiguation4/5

Most tools target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.

Naming Consistency3/5

Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.

Tool Count1/5

With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.

Completeness4/5

The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.

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