Skip to main content
Glama
ProSuite

prosuite-mcp

Official
by ProSuite

run_verification

Run ProSuite quality verification on geospatial datasets. Define conditions, apply them to a workspace, and get a per-condition error 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. Omitted, a local runs/ directory is used only when PROSUITE_HOST is local, since the path is resolved on the service's machine. 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

Behavior5/5

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

With no annotations, the description fully covers behavioral traits: it explains the output format including status, total_errors, and per-condition breakdown, error handling via 'status': 'error', and special behavior for output_dir (only defaults to local runs/ when PROSUITE_HOST is local). Also discloses the envelope spatial filter.

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?

The description is well-structured with paragraphs and bullet points, using clear labels like 'Args:' and 'Returns:'. Every sentence provides necessary detail without redundancy. It is concise yet comprehensive for a complex tool.

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, output schema not shown), the description covers prerequisites, all parameters with special cases, return format, and error handling. It leaves no critical gaps for an AI agent to invoke the tool correctly.

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?

The description adds substantial meaning beyond the input schema, which has 0% coverage. It explains each parameter's purpose, format, and examples: model_catalog_path as server workspace path, datasets with optional filter_expression, conditions with method names and params, output_dir with server-side write access caveat, and envelope as spatial filter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Run' and resource 'ProSuite quality verification', clearly stating its purpose. It differentiates from siblings like list_conditions and preview_condition_run by focusing on executing an ad-hoc condition list against a workspace.

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

Usage Guidelines4/5

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

The description explains the prerequisite environment variables (PROSUITE_HOST/PROSUITE_PORT) and the service reachability requirement. It does not explicitly state when to avoid this tool or suggest alternatives, but the context of building an ad-hoc condition list vs. running pre-defined specs is implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ProSuite/prosuite-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server