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by WenyuChiou
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

fit_check_apply

Filter candidates using AI scores and a configurable threshold, then write rejection sidecar for those below the threshold.

Instructions

Consume AI scores, filter candidates, write rejected sidecar.

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": {
      "cluster_slug": "<cluster_slug>",
      "candidates": "<candidates>",
      "scores": "<scores>"
    },
    "name": "fit_check_apply"
  }
}

Input Schema

NameRequiredDescriptionDefault
cluster_slugYes
candidatesYes
scoresYes
thresholdNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.4/5.0
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It only mentions writing a rejected sidecar, implying mutation, but lacks details on side effects, permissions, error handling, or the nature of the sidecar. Very minimal disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence, which is concise but at the expense of clarity. It could benefit from structure, but it is not overly verbose.

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?

Despite having 4 parameters (3 required) and an output schema, the description fails to explain the filtering logic, the 'sidecar' concept, or provide context for the tool's internal workings. Significant gaps remain given the complexity.

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

Parameters2/5

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

Schema descriptions coverage is 0%, and the description does not explain the parameters ('cluster_slug', 'candidates', 'scores', 'threshold'). The terms 'AI scores' and 'candidates' are used but not mapped to the schema. No added meaning beyond parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool consumes AI scores, filters candidates, and writes a rejected sidecar, giving a rough idea of the operation. However, 'sidecar' is undefined and the description does not differentiate from sibling tools like fit_check_audit or enrich_candidates, so clarity is moderate.

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 is provided on when to use this tool versus alternatives, nor are prerequisites or conditions mentioned. The agent receives no context about appropriate scenarios.

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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