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chc-social

chc-social

by CHCDOLADEEPSEEKMKSEC
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

social_recipes

Lists learned social posting recipes with their version and confidence so you can check which automated browser workflows are available and how reliable they are.

Instructions

List learned recipes and their version/confidence.

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": "social_recipes"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does signal that the operation reads existing state ('learned recipes') and that results carry version and confidence values, which hints at a non-empty structured return, but it says nothing about ordering, pagination, scope limits, or permissions.

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?

A single front-loaded sentence with no filler; the action and the returned attributes are stated in the fewest words possible. Nothing in it is redundant.

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

Completeness3/5

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

With no annotations, no output schema, and no parameters, the description is the only documentation, and it leaves key questions open: what a 'recipe' contains, whether results are paginated or bounded, and whether 'confidence' is a threshold the caller can act on. It is minimally adequate for a simple list call but not complete.

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?

The tool takes zero parameters, so the schema has nothing to document and there is no semantic gap for the description to close. The baseline for a parameterless tool applies.

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 uses a specific verb ('List') with a specific resource ('learned recipes') and names the fields surfaced ('version/confidence'), so an agent can tell this is a read of stored recipes rather than a mutation. It does not explicitly contrast itself with siblings such as social_learn, so it stops short of a 5.

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?

There is no when-to-use guidance: nothing says whether this should be called before social_learn, after social_post, or as a discovery step. The only hint is the word 'learned', which weakly implies reading previously acquired recipes rather than creating them, leaving the agent to infer the routing.

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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