Upsolve AI
Server Details
Query and join across SaaS tools, SQL, and NoSQL databases through one unified SQL interface.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion or overlap between tools.
The single tool name 'analyze_data' is clear, follows a verb_noun pattern, and is consistent with itself.
A single tool for data analysis is too limited; users likely need additional capabilities like listing data sources or managing analyses.
The tool only covers analysis, with no support for data ingestion, query management, or visualization export, which are essential for a complete data analysis workflow.
Available Tools
1 toolanalyze_dataAnalyze DataARead-onlyInspect
Analyze data using Upsolve AI agent. Ask questions about your data and get insights, SQL queries, charts, and visualizations. When presenting results, relay the agent's analysis to the user faithfully — preserve all data points, numbers, and conclusions verbatim. You may reformat for readability (markdown) but do not omit or reinterpret any information.
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | The analytical question or data request (e.g., 'Show me revenue trends for the last 6 months', 'What are the top products by sales?') | |
| thread_id | No | Thread ID for conversation continuity. On the FIRST call in a conversation, omit this field — a new thread_id will be returned in the response. On ALL subsequent calls in the SAME conversation, pass back the thread_id from the previous response. This groups related messages together in chat history. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, indicating safe, read-only behavior. The description adds valuable context: it instructs the AI agent to relay results faithfully, preserving all data points and conclusions. This goes beyond annotations by specifying how to present outputs. However, it does not describe any potential side effects or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two main parts: core functionality and behavioral instruction. It is front-loaded with the primary purpose. The second part about faithful relay could be considered slightly redundant as it instructs the AI rather than describing the tool, but it is relevant for correct usage. No wasted sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is no output schema, the description should clarify the return format (e.g., text, markdown, structured data). It mentions 'insights, SQL queries, charts, and visualizations' but does not specify how these are presented (likely as text with chart references). This leaves some ambiguity about what the AI agent will receive as a response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptive parameter names and examples. The description adds significant value by explaining the usage pattern for thread_id (first call omits, subsequent calls pass back) and providing example queries for message. This eliminates ambiguity about how to manage conversation continuity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool analyzes data using an AI agent, answering questions and providing insights, SQL queries, charts, and visuals. The verb 'analyze' and resource 'data' are specific, and the description distinguishes it from any hypothetical alternatives by focusing on open-ended data queries. However, the phrase 'Analyze data' is somewhat broad, lacking a more specific domain or data type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for asking questions about data, but it does not explicitly state when to use this tool versus alternatives or when not to use it. No sibling tools exist, so there is no competition, but the description lacks guidance on constraints or prerequisites like data format or size.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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