Neotic

cognitive_solve_and_compare

End-to-end autonomy: identify → guide → execute → baseline → verify → verdict.

Give raw task data (scheduling: workers/shifts/eligibility/capacity/
exclusivity; graph: nodes/edges; allocation: consumers/resources/...).
Returns the guided solution, the unguided baseline, independent
verification of both (with objective_source + independently_verified),
and whether the engine improved the result.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`task`	No
`model_family`	No		generic

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedSep 20, 2026
- removedInput schema / properties / task / additionalProperties
```
Removed value: -true
```
- addedInput schema / properties / task / anyOf
```
Added value: +[
+  {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]
```
- addedInput schema / properties / task / default
```
Added value: +null
```
- removedInput schema / properties / task / type
```
Removed value: -"object"
```
- removedInput schema / required
```
Removed value: -[
-  "task"
-]
```
AddedSep 19, 2026

TDQS

B3.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the disclosure burden, and it does disclose the multi-step pipeline: identify, guide, execute, baseline, verify, verdict. It also reveals that verification is independent and includes objective_source and independently_verified flags, which goes beyond a simple 'solves tasks' claim.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loads the purpose with a pipeline summary, then gives input expectations and return contents. The line-break formatting aids scannability, though the first sentence is somewhat jargon-heavy and '...' adds vagueness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex end-to-end tool, the description covers core inputs and outputs, and an output schema exists to fill in return details. However, it lacks guidance on model_family behavior and on how this tool relates to sibling solve/allocate tools, leaving some context necessary for fully correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, but it only partially does. It clarifies that the task parameter should receive raw task data and lists example fields, yet model_family is never explained, and neither parameter's optionality or default behavior is addressed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as an end-to-end solver that produces a guided solution, an unguided baseline, independent verification, and an improvement verdict. It also names concrete task domains (scheduling, graph, allocation), making the resource scope reasonably clear. However, it does not differentiate this tool from the very similarly named sibling cognitive.solve_and_compare.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when you have raw scheduling, graph, or allocation task data and want a guided solution compared against a baseline. It offers no explicit when-not-to-use guidance or alternatives, leaving the agent to infer selection among the many sibling solve and allocate tools.

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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