Tanod MCP Server (Deprecated)

by dev.tanod

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This connector has been deprecated

The correct URL is https://tanod.dev/mcp and NOT https://tanod.dev/mcp/ (CORRECT: NO TRAILING SLASH)

Correct Glama listing: https://glama.ai/mcp/connectors/dev.tanod/tanod

WRONG Glama listing: https://glama.ai/mcp/connectors/dev.tanod/tanod-mcp-server/

utilpeek: count words and score the readability of a text

text_stats

Read-onlyIdempotent

utilpeek: Count words and score the readability of a text. For word frequencies use text_frequency. Returns counts (characters, letters, words, unique words, sentences, paragraphs, averages), reading and speaking time, and for English the Flesch reading ease, Flesch-Kincaid grade, Gunning fog, SMOG, Coleman-Liau and ARI scores (estimates from heuristic syllable counts; Dale-Chall is not offered). An unsupported lang is a 422 invalid_lang and text without words a 422 no_words (not charged). Word counts undercount scripts written without spaces. Typically under 0.2 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/text-statistics-api.html

Input Schema

Name	Required	Description	Default
`lang`	No	ISO 639 code; readability scores are computed for English only (default en); any other language gets counts only.
`text`	Yes	The text (at most 200,000 characters).

Output Schema

Name	Required	Description
`lang`	No
`note`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`counts`	No
`source`	No
`syllables`	No
`readability`	No
`reading_time_seconds`	No
`speaking_time_seconds`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 3, 2026
- addedOutput schema / properties / readability / properties / flesch_reading_ease_label
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
First observedOct 22, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare a safe, idempotent read, and the description still adds substantial context: heuristic syllable-based scores, Dale-Chall intentionally absent, undercounting for scripts without spaces, ~0.2 s latency, pricing, free-tier limits, and that the text is never logged or stored. None of that appears in the annotations or schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and the sibling routing are front-loaded, then details follow in a single dense block. Nearly every clause carries operational value, though the pricing/free-tier sentence is the least essential for tool selection and the paragraph is long enough to be skimmed.

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?

For a two-parameter tool with rich annotations and an output schema, this covers what remains: supported languages, error codes, cost, latency, privacy, and known accuracy limitations. An agent has everything needed to call it and interpret results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both parameters are already documented, and the description largely restates the English-only readability rule for lang. It adds only the unsupported-lang error behavior and the script caveat on word counts, which is a modest increment over the structured fields.

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?

States a specific verb and resource (count words, score readability) and names exactly what it outputs, plus explicitly points to text_frequency for word frequencies so an agent can separate the two without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes word-frequency needs to the sibling tool text_frequency and defines the failure cases (422 invalid_lang, 422 no_words, uncharged), giving clear selection context. It does not mention adjacent siblings like text_sentiment, text_keywords, or text_summarize, so the routing is not exhaustive.

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

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