docs_get_textExtract plain text from a Google Doc without formatting or structure. Use when you need raw text content only.
| Name | Required | Description | Default |
|---|---|---|---|
| document_id | Yes | Document ID |
| Name | Required | Description | Default |
|---|---|---|---|
| text | No | Plain text content extracted from document | |
| error | No | Error code if connection failed | |
| title | No | Document title | |
| message | No | Error message if connection failed |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "error": {
+ "description": "Error code if connection failed",
+ "type": "string"
+ },
+ "message": {
+ "description": "Error message if connection failed",
+ "type": "string"
+ },
+ "text": {
+ "description": "Plain text content extracted from document",
+ "type": "string"
+ },
+ "title": {
+ "description": "Document title",
+ "type": "string"
+ }
+ },
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "document_id": "1BxiMVs0XRA5nFMKUVfGo6Q6XcYBCy_N1LCcFrZkZecc"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that extracting plain text results in no formatting or structure, which is a behavioral trait beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences front-loaded with action and purpose. Every word earns its place; no unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter, annotations covering safety, and an output schema, the description is complete for this simple tool. It clearly defines the tool's purpose and usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents the parameter. The description does not add additional meaning beyond what the schema provides, such as where to find the document ID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts plain text from a Google Doc without formatting or structure, distinguishing it from siblings like docs_get which likely return structured content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly mentions 'Use when you need raw text content only,' providing a clear use case. It implies not to use when formatting or structure is needed, though it could explicitly mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, while ask_pipeworx_grounded and deep_research heavily overlap with the same router. The dense Polymarket tool cluster and discovery tools (discover_tools vs suggest_questions) further blur boundaries, though many individual tools do have distinct niches.
Names are consistently lowercase snake_case, which helps, but the convention is mixed: some are verb_noun (docs_create, list_subscriptions), some are noun phrases (entity_profile, deep_research, bet_research), and one uses a suffix (ask_pipeworx_beta). It is readable but not a predictable pattern across the set.
37 tools is already above the 25+ threshold, but the bigger problem is that the server is named Google_docs and only 6 of the 37 tools relate to Google Docs. The remaining 31 tools form a broad data-research and prediction-market platform, making the set feel bloated and mislabeled for its apparent purpose.
For a Google Docs server, the surface is incomplete: you can create, read, insert, replace, and append text, but there is no delete, no list/search, no formatting control, and no permission handling. The extensive Pipeworx and Polymarket tools cover a different domain entirely, so they do not fill the gaps in the docs workflow.