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Tetrees AI

Execute an auditioned client-owned extension

execute_client_extension

Execute one method from an owned Pack through a user-configured HTTPS API, nested MCP server, or isolated local-skill child process. Configuration and credentials stay in the local MCP process. Write methods require an exact one-call confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
methodIdYes
argumentsNo
productIdYesTetrees AI Pack product id
extensionIdYes
writeConfirmationNoFor a write, use exactly: APPROVE extensionId.methodId

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

Discloses that write operations need a specific confirmation and that configuration/credentials remain local, adding context beyond the annotations. However, it does not mention potential side effects of executing external APIs or child processes, nor error handling or failure modes, given openWorldHint=true.

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 no redundant wording. The core action and key constraints are stated upfront, making it easy to parse.

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?

Lacks an output schema and does not describe response format, possible errors, or how to discover valid method names or extension IDs. The write confirmation template is helpful but does not clarify whether confirmation requires productId or how arguments are passed. Given the complexity of executing external code, this is insufficient for reliable 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?

Only 2 of 5 parameters have descriptions (productId and writeConfirmation); methodId, extensionId, and arguments are undocumented. The description does not compensate by explaining the relationship between productId, extensionId, and methodId, nor the structure of arguments. Agents may struggle to construct valid calls despite the writeConfirmation template.

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?

States clearly that it executes one method from an owned Pack via a user-configured HTTPS API, nested MCP server, or isolated local-skill child process. This distinguishes it from sibling tools like run_ai_pack or run_ai_pack_audition by emphasizing client-owned extensions and local execution contexts.

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?

Provides guidance on when to use: for owned Packs and user-configured execution endpoints. Notes that write methods require an exact one-call confirmation and that configuration/credentials stay local, which helps agents decide safety. Does not explicitly contrast with alternatives, but the phrase 'owned Pack' and execution types imply the intended scope.

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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TDQS

B3.1/5.0
Disambiguation4/5

Most tools are clearly distinct by action and resource, though several clusters (accept/propose/select/list growth, prepare/execute/run) require careful reading of descriptions to avoid confusion.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern, with clear action prefixes and descriptive resource nouns.

Tool Count2/5

With 26 tools, the set exceeds the recommended range and feels heavy, even though the domain covers publishing, runtime, growth, and reporting workflows.

Completeness4/5

The toolkit covers the main AI pack lifecycle—authoring, publishing, acquisition, running, searching, and reporting—but lacks explicit delete/revoke operations for drafts or entitlements.

Resources