Search Ross ERP 8.0 UI facilities by code, name, help text, program or tag. Use this to find the screen/report for a task, e.g. 'check register' or 'purchase order receipts'.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25) | |
| query | Yes | Search text, e.g. check register or AP_A |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the transparency burden. It discloses that the search can be based on multiple fields, but does not describe return behavior (what a result looks like, whether matches are partial or exact, pagination beyond the limit parameter). This is a noticeable gap for a search tool without an output schema.
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 exactly two sentences, with the primary action and scope in the first sentence and a practical usage example in the second. No wasted words; every sentence serves a clear purpose.
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 the moderate complexity of a search tool and the absence of an output schema, the description provides sufficient context for an agent to understand what to search and why. It does not describe the return format, but the tool's purpose is clear and the example use cases make it actionable.
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% and both parameters have descriptions. The description adds meaningful semantics by specifying that the query can match code, name, help text, program, or tag, which enriches the query parameter meaning beyond the schema's terse example.
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 the tool searches Ross ERP 8.0 UI facilities, specifying the searchable attributes (code, name, help text, program, tag). It also differentiates from siblings like search_programs by focusing on facilities/screens/reports, making the purpose unambiguous.
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?
It explicitly says 'Use this to find the screen/report for a task' with concrete examples ('check register', 'purchase order receipts'), providing clear when-to-use guidance. It does not mention when not to use it or alternatives, but the use case is specific enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: list/lookup/search pairs are separated by summary vs full detail, exact match vs fuzzy search, and domain scope. Cross-reference tools are explicitly paired as reverse lookups (facility_programs/program_facilities, table_facilities/table_programs), eliminating ambiguity. Minor overlaps like find_column vs search_columns and get_ddl vs lookup_table are well-differentiated by their descriptions.
The naming is largely predictable with list_ for browsing, lookup_ for full detail, search_ for searching, and _stats for overviews. Cross-links follow a noun_noun pattern (facility_programs, table_facilities). Exceptions like find_column, get_ddl, graph_neighbors, and path_between are still intuitive and do not create confusion, though they deviate from the dominant verb-first pattern.
At 21 tools, the set sits in the 16-25 range that feels heavy. However, each tool serves a distinct function across schema, facilities, programs, columns, and graph traversal, so the count is justified for a comprehensive data dictionary. It is not as tightly scoped as a typical CRUD server, but the breadth is necessary for the domain.
The tool set provides complete coverage for a read-only data dictionary: browsing, searching, full details, and cross-references for every entity type (objects, columns, facilities, programs), plus graph utilities. There are no obvious dead ends or missing operations; stats and search-all tools further enhance orientation and cross-domain discovery.