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Attach a Frank research to a product

link_product
Idempotent

Points an existing research study at an existing product. Use list_products for the product ID and list_researches for the research ID.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "researchId": "<researchId>",
      "productId": "<productId>"
    },
    "name": "link_product"
  }
}

Input Schema

NameRequiredDescriptionDefault
productIdYesThe product to attach it to.
researchIdYesThe research to attach.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnly=false, idempotent=true, destructive=false and closed-world, so the safety profile is covered. The description adds the meaningful precondition that both records must already exist, but says nothing about the effect of linking to a research that is already linked, or what a successful call returns.

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?

Two sentences, no filler, and the core action is front-loaded ahead of the ID-lookup hint. Nothing could be cut without losing information.

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 two-parameter link operation with full annotation coverage and no output schema, the description covers what the agent needs to call it correctly. The remaining gap is the post-call model (does this replace an existing product link, can it be undone), which matters for an agent reasoning about side effects.

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 100%, so the baseline is 3, but the description adds real provenance value: it tells the agent where each opaque string ID comes from (list_products, list_researches), which the bare schema descriptions ("The product to attach it to.") do not.

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?

"Points an existing research study at an existing product" states a specific verb (link/point) and both resources, and the word "existing" signals this is an association tool, not a creation tool. It is distinguishable from create_product/create_research, but it never explicitly says how it differs from edit_research, which could plausibly also touch the product association.

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 gives prerequisite routing (use list_products / list_researches to get the IDs), which is genuinely actionable. However it gives no when-to-use vs. when-not guidance, no statement about whether a research can be linked to multiple products, and no mention of what to do if the link already exists.

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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