text_extractTurn text into JSON matching a schema you give. Pass the fields you want as a JSON schema or a plain description; returns JSON only. Capped at 30,000 characters
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to read | |
| schema | Yes | A JSON schema, or a plain description of the fields wanted |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations beyond the title, the description carries the full burden. It usefully discloses 'returns JSON only' and the '30,000 characters' limit, but lacks details like error behavior or how malformed schemas are handled. It provides some behavioral context but is not comprehensive.
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?
The description is three concise sentences that front-load the purpose, then explain the invocation pattern, and end with a key constraint. No unnecessary words or repetition.
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?
The tool has no output schema, but the description clarifies that the output is JSON only, which covers the return format. It also specifies the input mechanism and character limit. Minor gaps include lack of schema format examples or failure modes, but overall it is sufficiently complete for a 2-parameter tool.
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 both parameters. The description's mention of 'JSON schema or a plain description' duplicates the schema description without adding new semantic detail. Baseline 3 is appropriate.
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's function: 'Turn text into JSON matching a schema you give.' This distinguishes it from sibling text tools like text_summarise and text_translate by specifying the structured extraction use case.
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 explains how to use the tool ('Pass the fields you want as a JSON schema or a plain description') but does not explicitly state when to use it over alternatives or when not to use it. Usage context is implied but not contrasted with sibling tools.
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.
Every tool is prefixed with a clear domain (e.g., apps_, blog_, transit_), and the suffix identifies a distinct action or resource. Overlapping tools like archive_search and news_search are explicitly differentiated in their descriptions.
All tools consistently use a domain_prefix_suffix pattern, but the suffix is sometimes a verb (create, list, search) and sometimes a noun (inbox, status, address). This minor mixing prevents a perfect score but remains predictable and readable.
With 113 tools, the count is far beyond the typical well-scoped range, even for a broad personal assistant. While each tool is distinct and serves a purpose, the sheer number is overwhelming and could be better organized into separate domain-specific servers.
Each domain has near-complete lifecycle coverage, including CRUD and search where relevant, with only minor gaps such as missing apps_delete or events_update. The wide range of covered domains itself demonstrates strong completeness for a general assistant.