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VelixarAi

Velixar MCP Server

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by VelixarAi

velixar_query_source

Query live data sources using natural language to retrieve real-time ground truth data. Your question is translated into SQL or API calls.

Instructions

Query a live data source through Marco Polo. Returns real-time data (ground truth). Natural language query is translated to SQL/API call. Always tag results as [LIVE DATA].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows to return (default 10, max 100)
queryYesNatural language query (e.g., "show me churn rate by quarter for the last year")
cursorNoPagination cursor for results beyond the limit
datasourceYesName of the data source to query (from velixar_list_sources)
show_queryNoReturn the generated SQL alongside results (default: false)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool queries and returns data, implying a read operation, but does not explicitly declare safety, idempotency, or side effects. It also lacks details on error handling, latency, or authorization requirements. The instruction to tag results is useful but insufficient for full transparency.

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

Conciseness4/5

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

The description is concise with three sentences, front-loading the core purpose and method. Every sentence adds value: purpose, guarantee of live data, and usage instruction. Slightly repetitive (live/real-time) but not wasteful. Could be tighter by merging sentences 2 and 3.

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 lack of an output schema, the description should provide more detail on the return format (e.g., rows, columns, JSON). It only guarantees 'real-time data' and a tag. The cursor parameter implies pagination but is not explained. For a query tool with 5 parameters, this leaves significant gaps in understanding what the agent receives.

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?

Since the input schema has 100% description coverage, the baseline is 3. The description adds marginal value by clarifying that the 'query' is natural language and translated, but this is already implied by the schema description. No additional semantics are provided for other parameters beyond what the schema offers.

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 queries a live data source via Marco Polo, returns real-time data, and translates natural language to SQL/API. This is specific enough to distinguish from siblings like velixar_list_sources (which lists sources) or velixar_search (which may not be live). However, it does not explicitly contrast with alternatives, so it falls short of a perfect 5.

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?

The description provides no explicit guidance on when to use this tool versus siblings. It mentions 'Always tag results as [LIVE DATA]' but does not explain prerequisites, exclusivity, or typical usage scenarios. Without such context, an agent may mis-select this tool for non-live data needs.

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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