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prosuite-mcp

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by ProSuite

run_verification

Run quality verification on geospatial datasets by specifying datasets, conditions, and optional spatial filter. Returns error summary and per-condition breakdown.

Instructions

Run a ProSuite quality verification.

Build an ad-hoc condition-list specification and run it against the given workspace. The ProSuite service (prosuite-qa-microservice) must be reachable at the host/port configured via PROSUITE_HOST / PROSUITE_PORT environment variables (default: localhost:5151).

Args: model_catalog_path: Workspace path on the server, e.g. 'C:/data/mydb.gdb' or a .sde connection file. model_name: Logical name for the data model (arbitrary, used in generated condition names). datasets: Feature classes or tables to make available for conditions. Each entry has a 'name' (feature class name) and an optional 'filter_expression' (SQL WHERE clause). conditions: Conditions to run. Each entry has: - condition: method name from list_conditions (e.g. 'qa_min_length_0') - params: dict mapping parameter names to values. Dataset parameters take a string matching a name in 'datasets'; primitive parameters take their direct value. output_dir: Optional server-side directory for Issues.gdb and HTML report. The service process must have write access. envelope: Optional spatial filter {x_min, y_min, x_max, y_max}. Omit for full-extent verification.

Returns a summary with status, total_errors, and per-condition breakdown. Check 'status': 'error' for connection or parameter failures.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetsYes
envelopeNo
conditionsYes
model_nameYes
output_dirNo
model_catalog_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Without annotations, the description carries full burden. It discloses service dependency, return summary, and failure modes (connection/parameter errors). This goes beyond basic functionality, though it omits details like rate limits or auth.

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 well-structured with a clear first sentence and helpful Args section, but it is somewhat verbose (e.g., environment variable details). It remains readable and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, nested objects, external service, output schema exists), the description covers setup, parameter details, and return summary. It feels complete and leaves no major gaps.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining each parameter, including structure for nested objects (datasets, conditions) and providing examples. This adds significant meaning beyond the raw schema.

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 explicitly states 'Run a ProSuite quality verification' and details building an ad-hoc condition-list. This is a specific verb+resource, but it does not explicitly contrast with sibling tools like load_spec or run_xml_verification, so it lacks differentiation.

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 description explains the ad-hoc nature and mentions prerequisites (ProSuite service, environment variables). However, it does not provide explicit when-not-to-use guidance or direct alternatives, leaving some ambiguity about when to choose this over siblings.

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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