Error code:   ConfigNamesError
Exception:    AttributeError
Message:      'str' object has no attribute 'items'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 690, in get_module
                  config_name: DatasetInfo.from_dict(dataset_info_dict)
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 284, in from_dict
                  return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
                                                 ^^^^^^^^^^^^^^^^^^^^^^^
              AttributeError: 'str' object has no attribute 'items'

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

MCP Servers Tool Catalog

Machine-introspected catalog of public Model Context Protocol (MCP) servers and the tools each one exposes. Each tool row contains the exact name, description, and JSON Schema input_schema captured by running the server and calling its tools/list JSON-RPC endpoint -- not metadata scraping.

Last extracted: 2026-05-18 Servers attempted: 922 Servers successfully introspected: 359 Tools captured: 9922

Why this dataset exists

The MCP ecosystem grew from ~1,200 servers in Q1 2025 to ~9,400 servers by April 2026, with 97M monthly SDK downloads. Every AI agent shipping MCP support (Claude Code, Cursor, Cline, Zed) faces the same problem: choosing servers blind. Existing datasets are insufficient:

Dataset	Servers	Tool defs?	Refresh	Downloads/mo
ScaleAI/MCP-Atlas	36	yes (benchmark subset)	static	2,588
DeepNLP/mcp-servers	296	metadata only	stale since 2025-03	~20
automatelab/mcp-servers-tool-catalog	922	yes (full schemas)	monthly	--

Three differentiators:

Actual tool introspection -- we run each server and capture its tools/list response, not its README.
Full coverage of npm-published MCP servers (vs. benchmark slices).
Monthly refresh with diff-based changelog (vs. static snapshots).

Schema

`tools` split

field	type	description
`server_name`	string	short slug for the server
`package`	string	exact npm package name
`tool_name`	string	`name` field from `tools/list`
`tool_description`	string	`description` field from `tools/list`
`input_schema`	string (JSON)	`inputSchema` field, JSON-encoded for parquet

`servers` split

field	type	description
`name`	string	short slug
`package`	string	npm package
`status`	string	`ok`, or one of the failure categories below
`elapsed_s`	float	wall-clock seconds for introspection
`tool_count`	int	tools captured (0 if not ok)
`server_info`	string (JSON)	server's response to `initialize`
`stderr_snippet`	string	last meaningful stderr line if introspection failed
`raw_status`	string	underlying state when classification couldn't pinpoint cause

Failure categories

Each non-ok row has a status from the following set, derived by matching the server's stderr against known patterns:

npm_404_not_found -- package does not exist on the npm registry
npm_network_error -- transient registry/network issue at install
needs_<vendor>_token / needs_<vendor>_key -- server hard-requires real credentials at startup (e.g. needs_stripe_key, needs_github_token)
needs_database_url / needs_aws_creds / needs_azure_creds / needs_google_creds -- backing-service credentials required
needs_config_file -- server expects a config file path
needs_cli_args -- server requires positional CLI arguments (e.g. a root path)
needs_python -- runtime depends on Python but python is not on PATH
broken_install -- npm install completed but the server fails to load (missing module, etc.)
init_timeout -- spawned, but did not respond to initialize within timeout
tools_list_timeout -- responded to initialize but not to tools/list

Methodology

For each candidate package:

Verify existence with npm view <pkg> name.
Spawn npx -y <pkg> over stdio with a JSON-RPC client.
Send initialize (protocol version 2024-11-05) and notifications/initialized.
Send tools/list, capture the response.
Classify stderr against ~17 known failure patterns if any step fails.
Write one row per server (always) and one row per tool (only on success).

Auth-gated servers were given dummy environment variables (STRIPE_SECRET_KEY=dummy, etc.). Most servers that list tools without hitting their backing service worked fine with dummies; those that validate credentials at initialize time get needs_<vendor>_* status.

The pipeline source is open: see introspect.py and validate_npm.py in the dataset repo.

License

Dataset additions: CC-BY-4.0. Each tool row attributes its source package to the upstream MCP server repository under its own license (see servers split).

Refresh cadence

Monthly. Each version tag includes a diff vs. the previous month (added servers, removed servers, new tools per server, removed tools).

How to load

from datasets import load_dataset
tools = load_dataset("automatelab/mcp-servers-tool-catalog", "tools", split="train")
servers = load_dataset("automatelab/mcp-servers-tool-catalog", "servers", split="train")

Acknowledgements

Built by automatelab.

Downloads last month: 59

Total file size:

3.22 MB