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Get AI Tell

get_ai_tell
Read-onlyIdempotent

Returns one AI Tells Index entry by id. Returns the whole record: what the pattern is, why it reads as machine-written, before and after examples, the detection rule, severity and status with its history, false-positive notes, model attribution, platform notes, and sources with an evidence grade. Takes an Index id (kebab-case) or the ai_tell_id of a draft-lint hit. A lint registry pattern id (snake_case, such as pointing_emoji) also works: it opens the entry that pattern links to, and resolved_from names the pattern id. A tell is a probabilistic signal of low-effort writing, never proof of AI authorship; false_positive_notes says when human writing trips it. Public reference data. No user data is read and nothing is written.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "get_ai_tell"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesThe entry id, kebab-case, from the Index listing or a lint hit's ai_tell_id. A lint registry pattern id resolves to its linked entry.

Output Schema

NameRequiredDescriptionDefault
urlYes
kindYes
tellYes
licenseYes
resolved_fromNo
dataset_versionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, closed-world and non-destructive, so the safety profile is free. The description still adds real context: it is public reference data, no user data is read, nothing is written, and — most importantly — a tell is a probabilistic signal, not proof of AI authorship, with false_positive_notes explaining when human writing trips it. That interpretive caveat is exactly the kind of thing an agent needs and annotations cannot convey.

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 is front-loaded in the first sentence and the id-resolution rules follow logically. The middle sentence enumerating every returned field (examples, detection rule, severity history, model attribution, sources with evidence grade) is long and partly redundant with the output schema, costing a point on economy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, read-only lookup with an output schema, the description covers the id forms, the resolution behavior, and the interpretive limits of a 'tell'. The payload enumeration is arguably unnecessary given the output schema, but nothing an agent needs in order to call it correctly is missing.

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?

Schema coverage is 100% and the schema already documents kebab-case ids and lint-hit resolution, so the baseline would be 3. The description goes slightly beyond by naming the resolved_from field that reports which pattern id was resolved, giving the agent a way to verify a non-canonical id was interpreted as intended.

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?

Opens with a specific verb and resource: "Returns one AI Tells Index entry by id." The singular 'one entry by id' contrasts implicitly but clearly with the sibling list_ai_tells, so an agent can route between browsing and fetching without opening either 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?

It spells out the three acceptable id sources (Index listing, a draft-lint hit's ai_tell_id, or a snake_case lint registry pattern id), which tells the agent exactly when this tool is callable. It never explicitly says 'use list_ai_tells to discover ids' or states an exclusion, so it stops short of full when/when-not 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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