Skip to main content
Glama

skills_run

Execute a skill on your LLM, optionally storing the output as a knowledge item, and get the result.

Instructions

Execute a Skill on the configured LLM, optionally storing the output as a knowledge_item, and returns the result. Available to all project members.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skill_idYes
input_paramsNoVariables substituted into the skill body
Behavior3/5

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

Annotations provide readOnlyHint=false (indicating mutation) and destructiveHint=false (suggesting no destruction). The description adds that execution is on 'the configured LLM' and optionally stores output as a knowledge_item. However, it does not disclose details like potential side effects, rate limits, or what happens on failure.

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?

The description is two short sentences that convey the essential purpose without extra words. It is front-loaded and efficient, earning every sentence.

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 no output schema, the description only says 'returns the result' without specifying format or content. It lacks information on error handling, timeouts, or prerequisites. For a tool with moderate complexity, this is insufficient.

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 description coverage is 50% (only input_params has a description). The description mentions 'optionally storing the output' but does not clarify any parameter for that option. It adds no semantic value beyond the schema, particularly for skill_id, which lacks a description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Execute a Skill') and resource, mentions the optional storage as knowledge_item, and indicates availability to all project members. However, it does not explicitly distinguish from sibling tools like routines_run_now, leaving some ambiguity.

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

Usage Guidelines2/5

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

The description only notes that the tool is 'Available to all project members,' but provides no guidance on when to use it versus other tools (e.g., routines_run_now) or when not to use it. There are no prerequisites or context for selecting this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/alfredoizdev/contextforge-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server