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secrets-mcp

secrets-mcp

by sealca
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

upsert_section

Add or update sections in a secrets vault to manage and organize confidential information using structured Markdown content.

Instructions

Add or update a section in the secrets vault

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": {
      "title": "<title>",
      "content": "<content>"
    },
    "name": "upsert_section"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleYesSection title
contentYesSection content (Markdown)
levelNoHeading level (1-6, default 2)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a mutation operation ('Add or update') but fails to detail critical aspects like whether this requires specific permissions, if updates are idempotent, what happens on conflicts, or any rate limits. This leaves significant gaps in understanding the tool's behavior.

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?

The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy for an AI agent to parse quickly.

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 complexity of a mutation tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral traits, error handling, return values, and usage context, making it incomplete for safe and effective tool invocation by an AI agent.

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?

The schema description coverage is 100%, so the input schema already documents all parameters (title, content, level) with descriptions and constraints. The description adds no additional meaning beyond what the schema provides, such as explaining how 'title' and 'content' interact or the implications of 'level'. Baseline 3 is appropriate as the schema handles the heavy lifting.

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 verb ('Add or update') and resource ('a section in the secrets vault'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'delete_section' or 'get_section' beyond the implied action difference, which keeps it from a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives such as 'delete_section' or 'get_section', nor does it mention prerequisites like authentication or context for updates versus additions. This lack of explicit usage instructions limits its helpfulness for an AI agent.

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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