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turingmindai

TuringMind MCP Server

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by turingmindai

turingmind_create_spec_node

Create atomic constraint DAG nodes with strict contracts and risk posture mapping to specify architecture.

Instructions

Create an atomic SpecNode in the constraint DAG. Every unit of work is represented as a SpecNode with a strict contract (inputs, outputs, invariants) and a surface_type for risk posture mapping. Architect Mode only: do NOT write code, only define constraints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoYesRepository (owner/repo)
levelYesL0=system, L1=file, ..., L6=phase (milestone), L7=project (cross-repo grouping)
titleYesShort human-readable title
node_idYesUnique deterministic ID for this constraint node
contractNoStrict mathematical contract: inputs, outputs, invariants, metrics
priorityNo
complexityNoRelative implementation complexity
effort_daysNoEstimated calendar days to complete
dependenciesNoIDs of upstream SpecNodes this node depends on
surface_typeNoRisk surface classification. api_endpoint nodes appear in Risk Posture Map.
intent_justificationNoRationale for why this node exists (e.g. from gap analysis)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It lacks disclosure of important behaviors such as whether the tool is idempotent, what happens if node_id already exists, or any authentication/permission requirements. The description only states the high-level purpose without behavioral details.

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

Conciseness5/5

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

The description is extremely concise with two sentences: the first states the primary function, and the second adds context and restrictions. Every word is purposeful with no fluff, making it easy to parse quickly.

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?

Given the complexity (11 parameters, nested objects, no output schema), the description is missing expected return value info and error handling semantics. For a creation tool, it would be helpful to know if it returns the created node or a confirmation. However, the description is adequate for a basic CRUD tool.

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 coverage is high (91%), and the schema descriptions already explain most parameters. The description reinforces the concept of 'strict contract' and 'surface_type' but adds no new semantic meaning beyond what is in the schema. Baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool creates an atomic SpecNode in the constraint DAG, distinguishes it from siblings like update or list, and specifies the 'Architect Mode only' context. The verb 'create' and resource 'SpecNode' are specific and unambiguous.

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

Usage Guidelines4/5

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

The description explicitly restricts usage to 'Architect Mode only' and clarifies not to write code but only define constraints. While it doesn't provide explicit when-not-to-use compared to siblings, the context is clear enough for an AI agent to differentiate.

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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