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OpenConstruction MCP

OpenConstruction MCP

by Ben11304
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Model Discovery

find_models

Locate machine learning models by filtering on task, dataset, modality, method, or publication evidence.

Instructions

Find models by task, dataset, modality, method, or publication evidence.

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": "find_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
taskNo
limitNo
methodNo
datasetNo
modalityNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior1/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states 'Find models by...' with no mention of return format, pagination, side effects, authorization requirements, or whether it is read-only. For a search tool, agents need to know if it returns a list, how results are sorted, etc. This is a significant omission.

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 description is a single sentence, concise and to the point. It front-loads the primary action ('Find models') and lists key filters. However, it's extremely brief and doesn't provide structure or additional helpful details. Still, it's not verbose and gets the core purpose across.

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

Completeness2/5

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

Given the tool has 5 optional parameters, no output schema, and no annotations, the description is insufficient. An agent doesn't know what the result looks like, how to interpret the filters, whether multiple filters can be combined, or what the default behavior is when no filters are provided. Also, the mention of 'publication evidence' is not in the schema, which could confuse the agent. The description is too sparse for a discovery tool with this much parameter variety.

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 0%, so the description must explain the parameters. It lists task, dataset, modality, method, but omits 'limit' and introduces 'publication evidence' which is not a parameter in the schema. The description gives minimal meaning to the parameters but doesn't explain how they combine, value formats, or edge cases. It provides some value but does not fully compensate for the lack of schema descriptions.

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?

The description clearly states the tool finds models and lists the primary filter dimensions (task, limit, method, dataset, modality). It distinguishes from siblings like find_datasets by naming the resource type (models). However, it mentions 'publication evidence' which is not in the schema, introducing slight ambiguity.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus siblings like find_datasets or search_resources. The description does not state when this tool is preferred, nor when it should not be used. The context implies model discovery, but no exclusions or alternatives are mentioned.

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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