Full schema for a Ross ERP 8.0 object (table / view / procedure / function): columns with type, nullability, keys, descriptions and valid values, foreign keys in and out, related tables, and usage.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Object name, e.g. GL_ACCOUNTS or ACCOUNT_TYPES |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, yet it does not explicitly state that the operation is read-only, nor does it mention error handling, permissions, or side effects. The name 'lookup_table' implies a read operation, but the description itself only lists return contents and offers no behavioral context beyond that.
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 a single, well-structured sentence that front-loads the core purpose ('Full schema for a Ross ERP 8.0 object') and then lists what's included. Every word adds value, and there is no redundancy or extraneous detail.
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 no output schema, the description does an excellent job of enumerating the return contents: columns with type/nullability/keys, valid values, foreign keys in/out, related tables, and usage. It covers all major aspects of a schema lookup. It falls short only by not mentioning error behavior (e.g., object not found), which is a minor gap for a well-scoped lookup tool.
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% with the 'name' parameter clearly described via an example ('GL_ACCOUNTS or ACCOUNT_TYPES'). The description adds no additional parameter semantics, but the schema already fully documents the parameter, so the baseline of 3 is appropriate.
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 what the tool returns: 'Full schema for a Ross ERP 8.0 object' with a detailed enumeration of schema components (columns, types, keys, FKs, related tables, usage). It differentiates from siblings like find_column or get_ddl by emphasizing the comprehensive schema scope rather than a specific column search or DDL generation.
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?
The description implies the tool is for retrieving complete schema details for a named object, but it does not explicitly state when to use this over alternatives such as find_column or list_objects. There are no exclusions or situational guidance, leaving usage to be inferred from the 'full schema' wording.
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.