get_helpGet the Datavrn agent guide: how connecting works (OAuth and API key), what an assistant can do, how reading a statement as data works, and the guarantees and limits — plus the current list of tools. Call this to answer a user's questions about how Datavrn works from canonical documentation instead of guessing.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnlyHint=true and destructiveHint=false. The description adds valuable context by specifying what the guide contains (OAuth/API key, assistant capabilities, statement-as-data, guarantees/limits, tool list) and that it serves as canonical documentation. This goes beyond the annotations without contradiction.
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 compact: two sentences, front-loaded with the tool's purpose, followed by a structured list of contents and a direct usage instruction. Every word earns its place, with no redundancy or padding.
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 the tool's simplicity (no parameters, no output schema), the description is fully sufficient. It explains what the guide covers, why to use it, and frames it as the canonical source for answering user questions, making it complete in context.
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 tool has zero parameters, which sets a baseline of 4. The description does not need to explain parameters and instead clarifies the tool's scope, maintaining the baseline.
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 uses a specific verb ('Get') and names a concrete resource ('the Datavrn agent guide'), then lists its contents. It clearly distinguishes from sibling tools that perform data operations, making this uniquely about documentation.
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 states when to use it: 'Call this to answer a user's questions about how Datavrn works from canonical documentation instead of guessing.' It provides clear context but does not mention any exclusions or alternative tools, so it earns a 4 rather than a 5.
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 targets a distinct resource or action — get_* reads, save_* writes, confirm_* approves, preview_* shows consequences before approval. Even the management-data trio (budgets, allocations, variance) is cleanly separated by surface. Two-step flows like preview_chart_rebaseline → confirm_complete_chart are clearly sequenced, so an agent won't confuse the stages.
Tool names follow a highly consistent verb_noun pattern: get_* for reads, list_* for discovery, save_* for section writes, confirm_* for approvals, create_* for new entities/centres, preview_* for pre-approval checks. The few one-offs (ingest_upload, upload_trial_balance, set_header_row) still fit the verb-first convention. No camelCase or style mixing.
At 67 tools this is well past the 'too many' threshold. While the Schedule III domain genuinely is broad — statutorily mandated sections, two-phase approval flows, readiness checks, and a separate management-data area — the surface is heavy; an agent will spend real effort just surveying the tool list. Some consolidation of the save_reserves/provisions/assets movements or merging preview+confirm pairs is possible.
The surface covers the full lifecycle: upload → mapping/costing → grouping → capture (all statutory sections) → declarations → readiness → generate → finalise → download, plus entity setup and consolidated statements. Minor gaps: no tool directly exposes historical version diffing beyond list_snapshots, and the management-data section (budgets, allocations, variance) feels bolted on rather than integral to the core flow.