Skip to main content
Glama
dipseth

google-workspace-unlimited

by dipseth
OverviewSchemaRelated ServersScoreDiscussions
Python

execute

Run sandboxed Python to invoke Google Workspace tools, chaining calls in one block to automate multi-step workflows.

Instructions

Run sandboxed Python that calls this server's Google Workspace tools via await call_tool(tool_name, params), chaining calls in one block. Use when: you know which tools to call. To find tool names first use search or tags; for exact parameters use get_schema; to look up past results instead, use semantic_search. Behavior: each call_tool runs the real tool immediately — sends, edits, and deletes take effect; there is no dry-run. Use return to produce output; prefer returning the final answer from a single block. Only call_tool(tool_name: str, params: dict) -> Any is available in scope. Unknown tool names raise NotFoundError; disallowed syntax raises SandboxError.

SANDBOX RESTRICTIONS — these produce SandboxError, avoid them:

  • sorted_(items, key=lambda x: x['k']) → lambda args fail; use builtins like key=len or sort manually

  • import only covers a small stdlib subset (e.g. json); no third-party modules — prefer the built-in helpers listed below

Built-in helpers (import is not available — use these instead):

  • now(tz_offset=0) → current datetime string (UTC by default)

  • today(tz_offset=0) → current date 'YYYY-MM-DD' (UTC by default)

  • days_ago(n, tz_offset=0) → ISO datetime string N days ago

  • hours_ago(n, tz_offset=0) → ISO datetime string N hours ago

  • format_date(iso_str, fmt='%Y-%m-%d %H:%M') → formatted date

  • parse_date(iso_str) → normalized ISO datetime

  • timestamp() → current unix timestamp (int)

  • to_json(obj, indent=None) → JSON string

  • from_json(s) → parsed object

  • url_encode(s) → URL-encoded string

  • url_decode(s) → URL-decoded string

  • url_join(base, *parts) → joined URL path

  • query_string(params) → URL query string from dict

  • re_find(pattern, text) → list of matches

  • re_match(pattern, text) → bool

  • re_sub(pattern, repl, text) → substituted string

  • truncate(text, n=80) → truncated with '...'

  • dedent(text) → remove common leading whitespace

  • wrap_text(text, width=72) → word-wrap to width

  • pad_left(s, width, char=' ') → right-justify / zero-pad

  • pad_right(s, width, char=' ') → left-justify

  • join(items, sep=', ') → joined string

  • html_escape(s) → HTML-safe string

  • sqrt(n), ceil(n), floor(n) → math

  • round_(n, digits=2), abs_(), min_(), max_(), sum_() → math

  • sorted_(items, key=None, reverse=False) → sorted list

  • unique(items) → deduplicated list (preserves order)

  • flatten(lists) → flat list from nested lists

  • counter(items) → dict of {item: count}

  • chunk(items, size) → list of chunks

  • zip_(*iterables) → zipped as list of lists

  • dict_get(d, 'a.b.c', default=None) → nested dict access

  • md5(s), sha256(s) → hash hex digests

  • gather_tools(calls) → run multiple tool calls sequentially; calls is a list of [tool_name, params] pairs, returns list of results (assign to variable, then index: r = await gather_tools([...]); a, b = r[0], r[1])

  • sleep(seconds) → async sleep

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesPython async code to execute tool calls via call_tool(name, arguments)

Tool Definition Quality

A5/5.0
Behavior5/5

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

The description is highly transparent about side effects and constraints: 'each call_tool runs the real tool immediately — sends, edits, and deletes take effect; there is no dry-run.' It also details sandbox restrictions, available built-in helpers, and error conditions (NotFoundError, SandboxError). With no annotations provided, this description fully compensates.

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?

Although the description is long, it is well-structured with bold headers for use cases, sandbox restrictions, and helper functions. Every section earns its place given the complexity of a code execution environment. The most critical usage guidance is front-loaded, and the detailed reference lists are clearly organized.

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 tool's complexity, the lack of annotations, and the absence of an output schema, this description is exceptionally complete. It covers the execution model, side effects, sandbox limitations, available helpers, and error handling. It provides all the information an agent needs to use the tool correctly without additional lookups.

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?

While the schema already describes the 'code' parameter, the description adds crucial semantic depth: the expected format (async Python code), the only available function in scope (call_tool), and the extensive list of built-in helpers. It also explains how to structure the code for chaining and return values. This goes well beyond the schema's basic description.

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 clearly states the tool's purpose: 'Run sandboxed Python that calls this server's Google Workspace tools via await call_tool(tool_name, params), chaining calls in one block.' This is a specific verb+resource description that distinguishes the tool from siblings by emphasizing its role as a code-execution orchestrator.

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?

The description includes explicit usage guidance: 'Use when: you know which tools to call.' It also directs users to alternatives for different needs: 'To find tool names first use search or tags; for exact parameters use get_schema; to look up past results instead, use semantic_search.' This clearly delineates when to use this tool versus other siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dipseth/google_workspace_fastmcp2'

If you have feedback or need assistance with the MCP directory API, please join our Discord server