Aident AI

skills_read

Read-only

Read the full entrypoint and manifest for one public text Skill selected by exact canonical identity and optional revision. After completing the task, optionally call skills_feedback with the Skill name, a 1-5 rating, and a concise issue when useful.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes
`parts`	No
`paths`	No
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`traversal`	No
`artifactRevision`	No
`artifactVersionId`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 14, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "name"
-]New value: +[
+  "name",
+  "context",
+  "llm_model"
+]

AddedAug 14, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful context ('public', 'optional revision'), but it does not disclose behavior around missing skills, invalid revisions, or how parts selection interacts with the 'full entrypoint and manifest' claim.

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?

Two sentences, front-loaded with the core function and selection criteria, followed by a useful but optional feedback directive. Every word earns its place, with no filler or redundant schema repetition.

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

Completeness2/5

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

Given 8 parameters, a nested traversal object, and no output schema, the description is too thin. It does not explain what manifest/instructions/references mean, what paths are, how traversal works, or what the return value looks like, leaving significant gaps for correct invocation.

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?

With only 25% schema description coverage, the description carries more responsibility. It does clarify that 'name' is a canonical identity and that a revision can be specified, but it leaves 'parts', 'paths', and 'traversal' unexplained — an agent would not know how to populate these just from the description.

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 states a specific verb ('Read'), a concrete resource ('the full entrypoint and manifest for one public text Skill'), and the selection criteria ('exact canonical identity and optional revision'). This clearly differentiates skills_read from its siblings like skills_search and skills_feedback without needing to inspect their schemas.

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

Usage Guidelines3/5

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

The description implies usage through 'selected by exact canonical identity' — an agent must already know the exact identifier — and it explicitly points to skills_feedback as a follow-up step. However, it never states when to prefer this tool over skills_search or skills_feedback, nor any exclusion conditions.

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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