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Mipiti

Mipiti MCP Server

by Mipiti
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Attach Foundation

attach_foundation

Bulk-delegate model objectives to a foundation's controls: get scored candidate pairs for review, or submit selections to create validation-pending draft edges.

Instructions

Delegate this model's objectives to a foundation's controls, in bulk.

Without selections it is read-only: it returns candidate (objective ↔ provider control) pairs with a match score, and nothing is created or credited. Show them to the operator.

With selections — a list of {"source_objective_id": ..., "provider_control_id": ...}, typically the confirmed subset of those candidates — it is mutating: each becomes a delegated draft edge that runs LLM validation and carries no credit until confirmed with manage_reliance(action="confirm"). Returns {created, failed}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesthe consumer model.
selectionsNothe pairs to delegate; omit to get the candidates.
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. Changed6 schema fields changedv0.84.0
    • addedInput schema / properties / selections / anyOf
      Added value: +[
+  {
+    "items": {
+      "additionalProperties": true,
+      "type": "object"
+    },
+    "type": "array"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / selections / default
      Added value: +null
    • changedInput schema / properties / selections / description
      Previous value: -"list of {source_objective_id, provider_control_id} dicts."New value: +"the pairs to delegate; omit to get the candidates."
    • removedInput schema / properties / selections / items
      Removed value: -{
-  "additionalProperties": true,
-  "type": "object"
-}
    • removedInput schema / properties / selections / type
      Removed value: -"array"
    • changedInput schema / required
      Previous value: -[
-  "server_version",
-  "model_id",
-  "foundation_model_id",
-  "selections"
-]New value: +[
+  "server_version",
+  "model_id",
+  "foundation_model_id"
+]
  2. Addedv0.66.0
  3. Removedv0.62.2
  4. First observedv0.57.0

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and discharges it well: it discloses that the no-selections path is read-only and creates/credits nothing, that the selections path is mutating, that edges start as 'delegated' drafts, that LLM validation runs, that no credit attaches until confirmation, and that the return is {created, failed}. This is unusually rich behavioral disclosure for a mutation tool.

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 dual-mode behavior is front-loaded and clearly partitioned by the 'Without selections' / 'With selections' structure, so every sentence earns its place. It runs a bit long, but the length is driven by necessary mutation and credit semantics rather than padding.

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

Completeness5/5

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

For a 4-parameter tool with a nested-ish selections payload and an output schema, the description covers the read-only vs mutating split, the credit/validation lifecycle, and the confirmation path, and it does not need to restate return values since an output schema exists. Nothing an agent needs to call it correctly is missing.

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

Parameters4/5

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

Schema coverage is 75% and the schema already labels model_id, foundation_model_id, and selections. The description adds real meaning beyond the schema for 'selections' by giving the exact pair shape ({source_objective_id, provider_control_id}) and describing the typical value (the confirmed subset of candidates). server_version remains unexplained in both places, keeping this short of a 5.

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 names a specific verb and resource ('delegate this model's objectives to a foundation's controls, in bulk') and immediately distinguishes the tool from siblings like declare_foundation and manage_reliance by framing it as a bulk attachment operation. An agent can tell what it does without opening the schema.

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

Usage Guidelines5/5

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

It gives explicit when-to-use guidance for both modes: omit 'selections' to get read-only candidate pairs, pass 'selections' to create delegated edges. It also names the follow-up tool ('manage_reliance(action="confirm")') needed to carry credit, which is exactly the routing an agent needs.

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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