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Mipiti
by Mipiti

Attach Foundation

attach_foundation

Create draft delegation edges for selected objective-control pairs, running LLM validation before any credit is granted. Returns created and failed counts.

Instructions

Create draft delegation edges for selected (objective, control) pairs.

selections is a list of {"source_objective_id": ..., "provider_control_id": ...} (typically the operator-confirmed subset of propose_attach_foundation). Each becomes a delegated draft edge that runs LLM validation; none carries credit until separately confirmed. Returns {created, failed}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesthe consumer model.
selectionsYeslist of {source_objective_id, provider_control_id} dicts.
server_versionYes
foundation_model_idYesthe foundation to delegate to.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.66.0
  2. Removedv0.62.2
  3. First observedv0.57.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It clearly states that edges are draft, run LLM validation, carry no credit until separately confirmed, and returns {created, failed}. This is solid behavioral disclosure, though it doesn't cover failure semantics or side effects in depth.

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 concise, front-loaded with the main action, and each sentence adds critical information: input shape, relationship to proposal, draft/validation behavior, credit semantics, and return value. No redundant filler.

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

Completeness4/5

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

For a tool with four parameters and an output schema, the description covers the essential input semantics, the draft nature, the validation behavior, and the return shape. It lacks some detail about error cases or prerequisites, but the references to propose_attach_foundation and separate confirmation provide enough workflow context.

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 75%, so the schema already explains most parameters. The description adds valuable detail for 'selections' by showing its expected structure and relationship to propose_attach_foundation, but it adds little meaning for model_id, foundation_model_id, or server_version beyond the schema.

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 states a specific verb and resource: it creates draft delegation edges for selected (objective, control) pairs. It also references the sibling propose_attach_foundation, placing this tool in context as operating on a confirmed subset of that proposal, which distinguishes it from related attach/propose operations.

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 gives clear usage context: selections are typically the operator-confirmed subset of propose_attach_foundation. This implies the tool is used after proposal and before final confirmation, but it does not explicitly state when not to use it or directly compare it to alternatives such as confirm_reliance.

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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