readabilityCompute readability scores (keyless, offline): Flesch Reading Ease (0-100, higher = easier) and Flesch-Kincaid Grade Level, with the underlying word/sentence/syllable counts. Uses a syllable-estimation heuristic.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to score. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "text": "The cat sat on the mat. It was a sunny day."
+ },
+ {
+ "text": "The implementation of sophisticated algorithms necessitates comprehensive understanding of advanced mathematical principles and computational methodologies."
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already include readOnlyHint and idempotentHint, and the description adds valuable context such as 'keyless, offline' and 'syllable-estimation heuristic,' which informs the user about determinism and dependencies. It also mentions the underlying counts returned, giving a clearer picture of behavior beyond the 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?
The description is two sentences, front-loaded with the main verb and resource, and every clause adds relevant information: the metrics, the scale, the counts, and the heuristic. No filler or redundancy exists.
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?
For a simple single-parameter tool with rich annotations and no output schema, the description is quite complete: it names the exact outputs (Flesch scores and counts) and provides a key detail about the heuristic. It could mention edge cases like empty text or non-English input, but these are not essential for basic 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% with the single 'text' parameter already described. The description does not add significant detail about the parameter itself, but it does explain that word/sentence/syllable counts are computed, which indirectly clarifies how the text is processed. This meets the baseline without adding new parameter-level semantics.
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 computes readability scores, specifically naming Flesch Reading Ease and Flesch-Kincaid Grade Level. It also distinguishes itself from siblings by highlighting keyless and offline operation, making the purpose unambiguous.
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 the tool is for computing readability scores when you need those metrics, but it does not explicitly state when to use this tool vs alternatives like text_stats. No exclusions or comparison to sibling tools is provided, but the purpose itself strongly suggests the usage scenario.
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.