Reverse of facility_programs: which UI facilities (menu screens / reports / inquiries) a program backs — 'what screens does this program run?'. One program often serves several facilities. Lookup by TITLE id (e.g. AP_I_001) or basename (e.g. ap_i_vendors); each facility carries the same declared / titleId match label.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Program id or basename, e.g. AP_I_001 or ap_i_vendors |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the output context ('one program often serves several facilities') and the nature of results ('each facility carries the same declared / titleId match label'), indicating a read-only query. It does not explicitly state safety or error behavior, but the clear query phrasing and output expectations are sufficient for a lookup tool.
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 inverse relationship, followed by lookup instructions and output characteristics. Every sentence adds value, and there is no redundancy or fluff.
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 lookup tool with one parameter and no output schema, the description explains the purpose, relationship, valid id formats, and output label match. It is mostly complete, though it could mention response structure or behavior when no matches are found, but these are minor omissions given its simplicity.
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 fully documents the parameter 'id' with examples (AP_I_001 or ap_i_vendors). The description repeats these exactly ('TITLE id (e.g. AP_I_001) or basename (e.g. ap_i_vendors)') without adding additional semantic meaning. Since schema coverage is 100%, the baseline of 3 applies.
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 purpose: 'which UI facilities... a program backs' and immediately contrasts with the sibling tool 'facility_programs', distinguishing it as the inverse operation. It uses a specific verb and resource, with examples of valid inputs.
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 mentions the reverse relationship to facility_programs, giving context on when to use this tool (when you have a program and want facilities). It also provides lookup by TITLE id or basename, which guides usage. However, it does not explicitly state when not to use it, hence 4 rather than 5.
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.