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bridge-agent

bridge-agent

by Erikdai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_skills

Retrieve skills learned from earlier successful runs to check for a matching recorded procedure, then replay it instead of planning from scratch.

Instructions

List the skills learned from earlier successful runs: name, task description, step count and success record.

A skill is a recorded cross-environment procedure that replay_skill can execute without a model. Check this before
planning a task from scratch; if a listed description matches the task, replay it.

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": "list_skills"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well: it defines a skill as a recorded cross-environment procedure that replay_skill can execute without a model, which is real context about the data's origin and semantics. It is a read-only listing so safety disclosure is trivial, but it says nothing about ordering, scope limits, or whether prior runs of other environments are included.

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?

Front-loaded with what is returned, followed by the definition of a skill and the routing advice; every sentence is doing work. The phrase 'learned from earlier successful runs' is echoed by 'success record' in the same sentence, a minor redundancy, but there is no filler.

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

Completeness5/5

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

An output schema exists so return-value detail is not required, and there are no parameters or annotations to compensate for. What remains — the meaning of a skill, the relationship to replay_skill, and when to call this first — is all present, so an agent has everything needed to call it correctly.

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?

Zero parameters, so there is no per-parameter semantics to explain; the baseline for a parameterless tool applies. Schema coverage is 100% and the description adds no syntax that could conflict or confuse.

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?

States a specific verb and resource ('List the skills learned from earlier successful runs') and enumerates the returned fields (name, task description, step count, success record). It also positions the tool against the sibling replay_skill by explaining that replay_skill executes what this lists, so an agent can tell the two apart without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit when-to-use ('Check this before planning a task from scratch') and a concrete follow-up condition ('if a listed description matches the task, replay it'), naming the alternative tool. Nothing about tool selection is left to inference.

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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