DECIDE. Return the full normalized scored matrix (per-option, per-criterion) + ranking when scores are supplied separately, without the winner narrative.
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | ||
| scores | Yes | ||
| options | Yes | ||
| criteria | Yes |
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 output scope (matrix + ranking, no narrative) and input condition (scores separate), but does not explain normalization details, input validation, or edge cases. The core behavior is transparent enough for a calculation tool, but lacks depth.
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 a single sentence, front-loaded with 'DECIDE' and immediately states the action and output. Every word earns its place, with no redundant filler.
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 4 parameters (3 required), nested objects, no output schema, and no annotations, the description is too brief. It does not explain the shape of the scores object, the role of options/criteria, or how normalization/ranking are computed. An agent would likely need additional context to invoke this correctly.
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?
Schema coverage is 0%, so the description must compensate. It hints that 'scores' contains per-option, per-criterion data and that scores are provided separately, but it does not explain the structure of 'options', 'criteria', or 'method'. This leaves significant gaps for agents needing to construct valid inputs.
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: returning a full normalized scored matrix per option and criterion, plus ranking. It also distinguishes itself from sibling tools by noting it works 'when scores are supplied separately' and omits the 'winner narrative,' making the purpose unambiguous.
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 provides clear context for when to use the tool: when scores are supplied separately and a full matrix/ranking is desired without a winner narrative. It does not explicitly name alternatives or exclusions, but the conditional phrasing effectively signals its niche among the decide_* family.
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.
Most tools have clear, distinct purposes across three namespaces (calc_, decide_, sim_) plus composites. Some conceptual overlap exists (e.g., decide_sensitivity vs. sim_sensitivity, decide_score vs. decide), but descriptions clarify the boundaries well.
Names follow a consistent snake_case convention with a namespace prefix (calc_, decide_, sim_) and a descriptive verb_noun structure. Even composite tools and utilities like health_check and list_capabilities fit the pattern.
24 tools is on the heavier side, but it's justified for a meta-server exposing three distinct engines plus cross-domain composites. The count is appropriately scoped for the breadth of capabilities advertised.
The set covers all core domains with discovery (list_capabilities, *_list_*), health_check, and composite tools linking simulation to decision and valuation. Minor gaps include lack of a template management tool, but sim_run accepts free-form models, mitigating this.