Score text 0 to 100 for chatbot habits like filler, em dashes and buzzwords, each one marked.
Scored across 1 tool
Only one tool exists, so there is no possibility of misselection between tools. Its purpose (checking a draft for chatbot writing habits) is stated unambiguously in the description.
The single name `tells_check_writing` is snake_case, prefixed with the server name, and follows a verb_noun-ish pattern, which is readable. With only one tool there is nothing to be inconsistent with, so consistency is trivially satisfied rather than demonstrated.
One tool is defensible for a tightly focused habit-checking service, but the surface feels thin for a standalone server. Agents will frequently want a companion action (e.g. rewrite/fix), which the description explicitly punts back to the caller.
The check operation itself is richly specified with scoring, verdicts and per-habit examples. However, the lifecycle dead-ends: there is no tool to apply fixes, batch-check multiple drafts, or configure which habits to flag, leaving notable gaps for the stated purpose.
Use when the user asks whether a piece of writing sounds like AI, wants an email, post, essay or cover letter checked for chatbot habits before sending, or wants to know which phrases to fix. Returns a 0-100 score, a verdict, and each habit found (chatbot words like "delve" and "tapestry", "not X, it's Y" framing, em-dash overuse, assistant filler, fake suspense, stiff transitions, markdown leftovers and more) with examples and why readers notice it. It is a habit check, not an AI detector, and short texts give rough scores. Example: {"text": ""}. To rewrite, use your own judgment on the flagged habits.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The writing to check (up to 60,000 characters). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is carried structurally. The description adds genuine context beyond that: the return shape (0-100 score, verdict, per-habit examples and rationale), the scope limit "not an AI detector", and the caveat that short texts give rough scores. However, it says nothing about rate limits, latency, or handling of the 60k character ceiling, so it is a solid but not exhaustive addition over 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?
Front-loaded with the trigger phrase "Use when..." and then the return contract, which is the right order for an agent scanning for relevance. The habit enumeration in the middle sentence is long but each item is concrete evidence of what gets flagged, so it largely earns its space; only the closing rewrite note is slightly padding.
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?
There is no output schema, so the description carries the return contract itself and does so fully: score range, verdict, per-habit detail with examples and rationale, plus an important reliability caveat for short inputs. For a one-parameter read-only check, an agent has everything needed to call it and interpret the result.
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% and the single parameter already documents its 60,000-character limit, so the schema does the heavy lifting. The description's inline example {"text": "<the draft to check>"} restates the invocation shape but adds no syntax or formatting guidance beyond the schema. Baseline 3 is correct for a fully documented single-parameter tool.
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?
States a specific verb and resource ("check writing for AI-sounding habits") and immediately draws the boundary that matters: "It is a habit check, not an AI detector." An agent knows exactly what class of operation this is and what it produces (score, verdict, flagged habits) without opening the schema.
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?
Explicit trigger conditions are given (user asks whether writing sounds like AI, wants an email/post/essay/cover letter checked before sending, wants phrases to fix), plus an explicit exclusion for detection and a redirect for rewriting ("use your own judgment on the flagged habits"). Nothing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
tells_check_writingtells_fix_texttells_score_texttells_fix_texttells_score_textScore text 0-100 for signs of AI writing, with plain reasons; strip machine typography.
Scores whether a page reads as AI-generated and names the tells to fix.
Score a public website 0 to 100 for the design habits that make it look AI-made.
Add one secure layer between your agents and this server.
