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fmagenticl_get_workarounds

Query collective friction advisories and verified community workarounds for a specific target package or infrastructure tool. Top supported targets: pydantic, litellm, crewai, langchain, langgraph, mcp, fastmcp, uv, gitlab-cli, workers-sdk, wrangler, docker, git, powershell, sqlite, vllm, llama-index, anthropic, transformers, fastapi, starlette.

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": "fmagenticl_get_workarounds"
  }
}

Input Schema

NameRequiredDescriptionDefault
targetNoTarget package or tool name (e.g. pydantic, litellm, crewai, langchain, mcp).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. 'Query' strongly implies a read-only retrieval operation, and the description adds that the workarounds are 'verified' and 'collective,' but it omits auth needs, rate limits, freshness, and result format.

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 definition is two sentences and front-loads the core purpose before the supported-target list. The target list is long but earns its place by directly helping with the only parameter.

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

Completeness4/5

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

For a low-complexity, one-optional-parameter query tool with no output schema or annotations, the description is nearly complete: it explains what is queried and which targets are supported. It could still say more about the expected result shape or operational behavior.

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?

Schema coverage is 100% and the single 'target' parameter is already documented, so the baseline is 3. The description adds value by listing many top supported targets beyond the schema examples, helping an agent know valid or common values.

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 uses a specific verb ('Query') and a precise resource ('collective friction advisories and verified community workarounds') scoped to a target package or infrastructure tool. It is clearly a retrieval tool and naturally distinguishable from sibling mutation or reporting tools like auto_heal, resolve, and report_telemetry.

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?

It gives clear context for use: query when you need advisories or workarounds for a specific target package or tool, with a helpful list of supported targets. It does not explicitly compare against alternatives such as fmagenticl_resolve or fmagenticl_auto_heal or state when not to use it.

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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