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list_installed

Read-only

Lists all MCP servers configured in your AI client, with environment variables masked. Helps you see existing setups before adding new servers or find exact server names for testing or removal.

Instructions

List all MCP servers currently configured in your AI client.

Use this to see what servers are already set up before adding new ones with configure_server, or to find the exact server name needed for test_connection or remove_server.

Secret-looking environment variable values (API keys, tokens) are automatically masked as "***" in the output.

Args: client: Which MCP client's config to read. One of "claude_desktop", "claude_code", "cursor", "windsurf". Auto-detects if empty. project_path: Optional project directory containing mcp-tap.lock. When provided, output is enriched with canonical identity fields (package_identifier, registry_type, repository_url) for matched servers.

Returns: List of configured servers, each with: name, command, args, env (masked), and config_file path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clientNo
project_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations indicate readOnlyHint=true, and description adds important behavioral detail about automatic masking of secret-looking environment variables. No contradictions.

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?

Description is well-organized with a clear first line, usage guidance, behavioral note, and parameter breakdown. Slightly wordy but efficient given the detail provided.

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?

Given the presence of output schema (context signal), the description adequately covers purpose, usage, behavioral details, parameters, and return structure. No gaps identified.

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

Parameters5/5

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

Schema has 0% description coverage, but description fully explains both parameters: client lists valid values (Claude Desktop, Claude Code, Cursor, Windsurf) and auto-detection behavior; project_path describes enrichment with additional fields. This adds critical meaning beyond the schema.

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?

Description clearly states it lists all configured MCP servers. The verb 'List' and resource 'servers' are specific, and the tool is distinct from siblings like configure_server, test_connection, and remove_server.

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?

Explicitly advises using this tool before adding new servers or to find server names for other tools. Also mentions auto-detection of client and optional enrichment with project_path.

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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