Reverse lookup: which UI facilities (screens/reports/programs) touch a given Ross ERP 8.0 table — split into core (facilities whose working set includes it) and reference (facilities that use it as a control/lookup), each with how many times the program source accesses it. Answers 'what screens read or write this table?'.
| 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?
Since no annotations are provided, the description carries the full burden. It discloses the output structure (core vs. reference, access counts) and clarifies what 'touch' means. It does not explicitly state read-only behavior, but the nature of a reverse lookup and the wording 'accesses' imply a safe query 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 with no wasted words. It efficiently packs the reverse lookup purpose, the core/reference split, and the access count detail in a structured dash clause. The final question reinforces the tool's intent without redundancy.
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 single-parameter lookup tool with no output schema, the description is adequately complete. It tells the user exactly what the tool returns (facilities split by core/reference with counts) and answers the primary use case. No significant behavioral detail is missing.
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 schema already provides full coverage for the single 'table' parameter with an example (GL_ACCOUNTS). The description only adds the Ross ERP domain context and does not introduce new parameter-level semantics beyond the schema, matching the baseline of 3.
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 a reverse lookup from a given Ross ERP 8.0 table to UI facilities, and further specifies the split into core and reference with access counts. The 'Reverse lookup' phrase and the question 'what screens read or write this table?' effectively distinguish it from forward lookup tools like lookup_table and table_programs.
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 usage: when you have a table and want to know which facilities (screens/reports/programs) use it, with a clear breakdown. It does not explicitly name alternatives, but the 'reverse lookup' framing and the answerable question provide clear context for when to invoke this tool.
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.