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ProbeLine

identify implant

identify_implant
Read-only

Rank likely dental implant systems from observed features (connection type, platform/body diameter, body shape, bone/tissue level), with reasons for each candidate. Costs $0.10 USDC per call (x402, Base).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "identify_implant"
  }
}

Input Schema

NameRequiredDescriptionDefault
levelNo
limitNo
body_shapeNo
connection_typeNoe.g. 'internal hex', 'external hex', 'conical', 'morse taper', 'tri-channel'
body_diameter_mmNo
manufacturer_hintNo
platform_diameter_mmNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Adds real behavioral context beyond the readOnlyHint/openWorldHint annotations: every call costs $0.10 USDC via x402 on Base, which tells the agent this requires a payment-capable client and should not be called casually. It also states each candidate comes back with reasons, revealing the response shape. It does not say whether all parameters are optional or what happens with an empty payload.

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 with no filler; the function and its inputs come first and the pricing caveat is placed last where it can't obscure the purpose statement.

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?

With no output schema, the description usefully says results are ranked candidates with reasons, and the pricing model is disclosed. The main gap is operational: with zero required parameters it never says how few features are needed for a meaningful ranking or how 'limit' behaves.

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 description coverage is only 14%, so the description must carry the burden, and it does: it enumerates connection type, platform/body diameter, body shape, and bone/tissue level, covering five of the seven parameters. Only 'limit' and 'manufacturer_hint' remain unexplained, and both are largely self-describing.

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?

States a specific verb (rank) and resource (dental implant systems) and enumerates the observed features that drive the ranking. That implicitly separates it from search_implants and get_implant, but it never names an alternative, so differentiation is inferred rather than stated.

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 phrase 'from observed features' implies this is the reverse-lookup/identification path rather than a direct lookup, which gives some usage context. There is no explicit when-to-use or when-not-to-use guidance, and no mention of when an agent should prefer search_implants or get_implant instead.

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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