text_statsCompute text statistics (keyless, offline): character counts (with/without spaces), word/sentence/paragraph counts, estimated reading time (200 wpm), average word length, and the longest word.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyze. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "text": "The quick brown fox jumps over the lazy dog. This is a sample sentence."
+ },
+ {
+ "text": "Artificial intelligence is transforming industries. Machine learning models require large datasets. Data preprocessing is crucial for model performance. Feature engineering improves prediction accuracy."
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint and idempotentHint. The description adds useful behavioral context beyond annotations: 'keyless, offline' and the specific reading time rate (200 wpm), which informs the agent about no network dependencies and computation parameters. It does not disclose edge cases or language limitations, but the added details are valuable.
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 a single, well-structured sentence that front-loads the core purpose and then lists specific statistics. Every phrase adds value, and there is no redundant or filler content.
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 is simple (one parameter, no output schema). The description compensates for the missing output schema by enumerating all computed statistics (character counts, word/sentence/paragraph counts, reading time, average word length, longest word). It also includes operational context ('keyless, offline') sufficient for an agent to invoke correctly.
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 description coverage is 100%; the only parameter 'text' is described as 'The text to analyze.' The description does not add additional parameter semantics, such as format constraints, encoding, or length limits. Baseline of 3 is appropriate since the schema already documents the parameter fully.
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 purpose with a specific verb ('Compute') and resource ('text statistics'), and enumerates exactly which statistics are computed. This distinguishes it from siblings like 'readability' by naming specific outputs (reading time, word counts) and the 'keyless, offline' qualifier.
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 implies usage for any text analysis need but does not explicitly state when to use versus alternatives such as 'readability'. No when-not or alternative guidance is provided. The context is clear enough for a simple utility, but explicit exclusions or comparisons are lacking.
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.
Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants, deep_research, and bet_research all serve data retrieval with subtle differences. The descriptions help distinguish them, but the sheer number of similar tools creates ambiguity.
Tool names are mostly snake_case with a verb_noun pattern (e.g., resolve_entity, validate_claim). A few are nouns like 'readability' or 'text_stats', but the overall style is consistent and readable.
With 33 tools, the server is overstuffed. The server name 'Textstats' suggests a narrow focus, but it covers diverse domains (Polymarket, SEC, memory, subscriptions), making it feel bloated and unfocused.
The tool set covers a wide range of data sources and actions, but there are notable gaps for a 'text stats' server—only two tools directly handle text analysis. Additionally, obvious operations like a simple stock quote tool are missing, relying on ask_pipeworx instead.