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skills_search

Read-only

Search Aident-curated public text Skills. Results contain contextual snippets only; call skills_read to pick a result and load its full entrypoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNo
tagsNo
limitNo
queryYes
cursorNo
contextYesExplain 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."
categoryNoCategory returned by skills_search
llm_modelYesThe 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.
referencedCapabilityNamesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • 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 / properties / sort / enum
      Previous value: -[
-  "relevance",
-  "popular"
-]New value: +[
+  "relevance",
+  "popular",
+  "favorites",
+  "views"
+]
    • changedInput schema / required
      Previous value: -[
-  "query"
-]New value: +[
+  "query",
+  "context",
+  "llm_model"
+]
  2. Added

TDQS

A4/5.0
Behavior4/5

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

Beyond readOnlyHint=true and destructiveHint=false annotations, it discloses that results are 'contextual snippets only' rather than full entries and that a second call is needed for the full entrypoint. This adds meaningful behavioral context without contradicting annotations.

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, with the action and scope in the first and the result/next-step behavior in the second. Every clause earns its place and the most important routing information is front-loaded.

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

Completeness3/5

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

For a 9-parameter search tool with no output schema, the description conveys the core workflow (search → snippets → skills_read) and read-only safety, but omits pagination, sorting/filtering semantics, and the shape of snippet results. It is adequate for a simple call but leaves gaps for full parameter use.

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

Parameters2/5

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

Schema covers only 33% of parameters with descriptions, and the tool description names no parameters. While names like query, limit, and sort are self-evident, 'referencedCapabilityNames' and the interaction of tags/category/cursor are left undocumented, and the description does not compensate for the coverage gap.

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 opens with the specific verb 'Search' and the resource 'Aident-curated public text Skills', and immediately distinguishes itself from skills_read by stating results are snippets only and directing the agent to skills_read for full entries. This is a clear, specific purpose that differentiates from the most relevant sibling.

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 provides explicit routing: after a search, 'call skills_read to pick a result and load its full entrypoint', which tells the agent the follow-up tool. It does not enumerate exclusions or compare to capabilities_search, so it stops short of full when/when-not coverage.

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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