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mcp-bw-adt-api

by lingcSun
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TypeScript
Remote

bw_replication_info

Pre-check a DataSource for replication readiness, identifying issues before replication starts so you can avoid failed runs.

Instructions

Replication pre-check for a DataSource.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasourceYes
outputPathNoIf provided, the full result is written to this path (under the workdir) and the tool returns only a small summary envelope { ok, outputPath, bytes, summary }. Objects/arrays are written as JSON; ONLY the bw_*_get_xml family writes the raw XML string as plain text (that file is directly reusable as xmlPath in a later save call). Other *_get tools (e.g. bw_dtp_get, bw_trfn_get) write the PARSED XML-to-JSON tree, not raw XML — use the matching bw_*_get_xml tool when you need the raw XML string. Use outputPath for large responses (XML, table data, logs, dataflow graphs). When set, tools skip inline pagination/projection so the file contains the complete payload.
sourceSystemYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the one-sentence description carries the full burden of behavioral disclosure. 'Pre-check' suggests a read-only validation, but the description does not state whether it mutates anything, what conditions it verifies, what it returns, or what side effects (if any) it has.

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 a single focused sentence with no filler or repetition tangent. It is front-loaded and to the point, though it sacrifices substantive detail for brevity.

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?

With no output schema, no annotations, and only a minimal description, there is not enough context for an agent to know what the pre-check returns, how to interpret success or failure, or what prerequisites must exist. The tool is simple, but the description still lacks important behavioral and output context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33% and the description does not compensate. The required parameters 'sourceSystem' and 'datasource' are self-explanatory by name, but the description adds no additional meaning about their format, allowed values, or relationship to each other beyond identifying the DataSource itself.

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, 'Replication pre-check for a DataSource,' identifies the operation ('pre-check') and the resource ('DataSource') clearly, and it is more specific than the tool name alone. It is not a tautology, but it does not explicitly differentiate itself from siblings like bw_replication_replicate or bw_replication_replicate_full.

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

Usage Guidelines3/5

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

The word 'pre-check' implies the tool should be used before performing replication, but the description never states this explicitly or names any alternative. There is no 'use when' or 'instead of' guidance, leaving the agent to infer the intended workflow.

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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