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

Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It does describe the output components (guided solution, unguided baseline, independent verification with objective_source and independently_verified, and improvement verdict), which is useful. It does not mention side effects, required permissions, or whether it mutates state, though it likely is a pure computation. It also does not mention any potential failures or limitations. Overall, it provides moderate transparency about what the tool returns but not about its operational characteristics.

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 concise: two short paragraphs. The first sentence uses a chain of steps to convey the process, which is compact but slightly cryptic. The second sentence clearly lists input types and output components. It is front-loaded with the core purpose and provides necessary details without excessive verbosity. The use of jargon like 'objective_source' and 'independently_verified' might be unfamiliar, but they are explained in context.

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?

The tool is complex, but an output schema exists, so return values are covered elsewhere. The description covers the input domains and the output components. However, it leaves 'model_family' unexplained and does not give an example invocation or clarify what 'improved' means in the verdict. It also does not address any prerequisites or constraints. For a tool with such broad scope, the description is adequate but not complete.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must explain the parameters. It explains the 'task' parameter well with examples (scheduling: workers/shifts/eligibility/capacity/exclusivity; graph: nodes/edges; allocation: consumers/resources/...), which gives an agent a good idea of what to pass. However, it does not explain 'model_family' at all, leaving that parameter undocumented. Since there are only two parameters and one is partially covered, the description only partially compensates for the lack of schema documentation.

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 states the tool's purpose: it solves a task both with and without guidance, then verifies and compares the results. It names the resource (raw task data) and the process (identify → guide → execute → baseline → verify → verdict), which is clear enough. However, it does not use a simple verb+resource structure, and the phrase 'End-to-end autonomy' is vague. It distinguishes itself from more specific solvers like solve_arithmetic by covering scheduling/graph/allocation domains, but not by explicit naming.

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 it: when you have raw task data from scheduling, graph, or allocation domains and want a guided vs. unguided comparison with verification. It does not explicitly list alternatives or say when not to use it. Since many sibling tools exist for specific problem types, the lack of exclusion guidance is a gap. The 'End-to-end autonomy' phrase suggests it replaces multiple steps, but this is not explicit.

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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