Look up a single column by exact name across the whole Ross ERP 8.0 schema: its description, valid values, notes, every table that carries it, and any foreign-key roles it plays.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Column name, e.g. POSTING_DATE or GL_ACCOUNT |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the matching behavior ('exact name'), scope ('whole schema'), and the return contents (description, valid values, notes, tables, foreign-key roles). However, it doesn't mention what happens if the column is not found, or whether matching is case-sensitive, which are minor gaps. Overall, it provides substantial behavioral context beyond a simple query.
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 one sentence, front-loaded with the verb 'Look up', and packs all essential information without redundancy. Every element earns its place: action, resource, scope, exactness, and the list of returned details. There is zero waste.
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 thoroughly explains the return values: 'description, valid values, notes, every table that carries it, and any foreign-key roles.' It is complete for typical use cases. It could be more complete by addressing not-found behavior, but given the tool's simplicity and the rich return description, it is nearly complete.
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 documents the single 'name' parameter with an example (100% coverage). The tool description adds the 'exact name' requirement, which clarifies matching semantics and excludes wildcard or partial matches. This adds meaning beyond the schema's bare example, so a 4 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: 'Look up a single column by exact name across the whole Ross ERP 8.0 schema.' It specifies the resource (column), the action (look up), the scope (whole schema), and the exactness criterion. This distinguishes it from sibling tools like search_columns (likely fuzzy) and lookup_table (table-level).
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 gives clear context: use this when you need a column by its exact name and want comprehensive information across all tables. It does not explicitly mention alternatives or when-not-to-use, but the 'exact name' wording implies a contrast with fuzzy search siblings. A 5 would require explicit exclusions or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.