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Analyze Writing

analyze_writing
Read-onlyIdempotent

Analyze a draft for readability, passive voice, cliches, and hedging. FREE.

Also measures sentence variety and keyword density, and produces a prioritized fix list. Typical input {"text": ""} returns {"readability_grade": 9.2, "passive_voice_count": N, "cliches_found": [...], "hedging_words": [...], "sentence_count": N, "avg_words_per_sentence": N, "top_repeated_words": [...], "priority_fixes": ["..."]}.

Use on body prose to find readability and style problems. Not for ranking titles (headline_analyzer) and not for platform limits (social_length_check). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe draft to analyze — at least one full sentence; plain text, any length.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The description adds crucial behavioral details beyond readOnly and idempotent annotations: it never raises protocol errors but returns an error object, and includes sample output structure. This enriches the agent's understanding of expected behaviors.

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

Conciseness5/5

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

The description is well-structured with a clear purpose, example output, usage notes, and error handling, all in a compact form. Every sentence provides value, and key points are front-loaded.

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 single-parameter tool with full schema coverage and an output schema, the description is comprehensive: it covers usage, errors, retry safety, and sample output, leaving little ambiguity.

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

Parameters4/5

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

The schema covers 100% of the parameter, and the description reiterates the typical input format and adds constraints like 'at least one full sentence' and 'plain text', which go beyond schema essentials.

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?

The description clearly states the tool analyzes drafts for readability, passive voice, cliches, and hedging, and lists additional metrics. It distinguishes itself from siblings by explicitly noting it's not for titles or platform limits.

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

Usage Guidelines5/5

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

It explicitly says to use on body prose and not for ranking titles or checking social length limits, naming alternatives (headline_analyzer, social_length_check). This is clear when-to-use and when-not-to-use guidance.

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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