Reverse lookup: which standard Ross ERP 8.0 programs reference a given table (validated against the Data Dictionary). Answers 'what code touches this table?' — complements table_facilities (which answers 'what screens touch it?'). Metadata only — no source body.
| Name | Required | Description | Default |
|---|---|---|---|
| table | Yes | Table name, e.g. GL_ACCOUNTS |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It states that the lookup is validated against the Data Dictionary and that it is 'Metadata only — no source body', which clarifies important behaviors. It does not explicitly mention being read-only, but 'reverse lookup' and 'metadata only' strongly imply a safe read operation.
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 two sentences, front-loaded with the core purpose and then providing differentiation and limitations. Every phrase adds value: the reverse lookup concept, the Data Dictionary validation, the complement to table_facilities, and the metadata-only caveat.
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?
For a simple one-parameter lookup tool with no output schema, the description covers the key aspects: what it returns (programs referencing the table), validation, and scope (metadata only). It could explicitly state the output format (e.g., a list of program names) but the purpose makes this obvious. Overall, it is complete enough.
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?
The input schema already provides 100% coverage of the single parameter 'table' with a descriptive example. The description does not add parameter-specific details, but the schema fully documents the semantics. Baseline 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 the tool's function with a specific verb phrase ('Reverse lookup') and resource ('standard Ross ERP 8.0 programs referencing a given table'). It also distinguishes itself from the closely related sibling tool table_facilities, making the purpose unmistakable.
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 explicitly answers the intended use case ('what code touches this table?') and names a complementary tool (table_facilities) with a clear contrast. It does not mention other possible alternative tools or edge cases, but the guidance is sufficient for selection.
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.