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totem-mcp

totem-mcp

by emiliano-go
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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rejected_idea_create

Record a rejected idea as a memory to prevent future re-proposals. Stores the reason for rejection and relevant context.

Instructions

Create a rejected idea memory. Prevents re-proposing dead ends.

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>",
      "statement": "<statement>",
      "tags": "<tags>",
      "proposal": "<proposal>",
      "reason_rejected": "<reason_rejected>"
    },
    "name": "rejected_idea_create"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsYes
titleYes
detailsNo
projectNo
proposalYes
statementYes
replacementNo
reason_rejectedYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.1

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, and the description only mentions creating a memory. It does not disclose side effects (e.g., whether it overwrites existing memories, tags required, or how it integrates with other memory functions). Without this information, agents cannot anticipate the tool's full impact.

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 extremely concise (two sentences) with no unnecessary words. It packs the core action and purpose into a minimal, well-structured form, making it easy 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 tool's complexity (8 parameters) and the lack of parameter explanations, the description is insufficient for an agent to fully understand how to use the tool correctly. It omits crucial context about parameter relationships, required vs. optional fields, and expected output behavior.

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

Parameters1/5

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

The schema has zero descriptions for any of the 8 parameters. The description text does not explain the meaning of fields like 'proposal', 'reason_rejected', 'replacement', or 'project'. Agents are left to guess parameter semantics, which is critical for proper invocation.

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

Purpose5/5

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

The description clearly states the action (Create), the specific resource (a rejected idea memory), and the purpose (Prevents re-proposing dead ends). This distinguishes it from other memory tools in the sibling set that target different content types (e.g., gotcha, assumption).

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

Usage Guidelines4/5

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

The phrase 'Prevents re-proposing dead ends' gives a strong contextual clue for when to use this tool (when an idea has been rejected). However, it does not explicitly contrast with alternative tools like the generic memory_create_tool, leaving some inference required. Still, the purpose is clear enough to guide selection.

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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