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Qumge: Agent Capabilities & Skills

get_skill

Fetch the full SKILL.md content of one skill, by the slug returned from search_skills. Write the returned markdown to .claude/skills//SKILL.md (or the equivalent for the user's agent). If the reply says the skill ships more files (REFERENCE.md, scripts/…), call again with include_files: true to get them and write each next to SKILL.md.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe skill's slug, e.g. 'AgriciDaniel/claude-seo/seo-google'. Comes from search_skills.
include_filesNoAlso return the full text of the other files in the skill's folder. Off by default — they can be hundreds of KB. Use it when installing.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
slugYes
filesNo
skill_mdYes
source_urlNo
include_filesNo
skipped_filesNo
skills_page_urlNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / qumge_key
      Removed value: -{
      -  "description": "Optional: your Qumge API key, for clients that cannot set an Authorization header. Takes precedence over the header. Never echoed back.",
      -  "type": "string"
      -}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "files": {
      +      "items": {
      +        "properties": {
      +          "content": {
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "path": {
      +            "type": "string"
      +          }
      +        },
      +        "required": [
      +          "path"
      +        ],
      +        "type": "object"
      +      },
      +      "type": "array"
      +    },
      +    "include_files": {
      +      "type": "boolean"
      +    },
      +    "name": {
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "skill_md": {
      +      "type": "string"
      +    },
      +    "skills_page_url": {
      +      "type": "string"
      +    },
      +    "skipped_files": {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    "slug": {
      +      "type": "string"
      +    },
      +    "source_url": {
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "slug",
      +    "name",
      +    "skill_md"
      +  ],
      +  "type": "object"
      +}
  3. Changed1 schema field changed
    • addedInput schema / properties / qumge_key
      Added value: +{
      +  "description": "Optional: your Qumge API key, for clients that cannot set an Authorization header. Takes precedence over the header. Never echoed back.",
      +  "type": "string"
      +}
  4. Changed1 schema field changed
    • addedInput schema / properties / include_files
      Added value: +{
      +  "description": "Also return the full text of the other files in the skill's folder. Off by default — they can be hundreds of KB. Use it when installing.",
      +  "type": "boolean"
      +}
  5. First observed

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the burden. 'Fetch' implies a read operation and the description adds useful output-handling context (write to SKILL.md, call again with include_files if extra files are indicated). However, it does not state auth needs, rate limits, error behavior, or explicit read-only safety, leaving clear gaps.

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?

Front-loads what the tool does, then gives output-handling and conditional-call guidance in three efficient sentences. No filler, though the file-writing instruction is slightly more workflow guidance than core tool definition.

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

Completeness5/5

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

Output schema exists, so return values need not be explained, and complexity is low. The description covers purpose, slug source, output handling, and the optional include_files path, making it complete for an agent to invoke correctly.

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%, so baseline is 3, but the description adds meaning beyond the schema: it explains that slug comes from search_skills and gives a concrete trigger for include_files (when the reply says the skill ships more files).

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 a specific verb and resource: fetch the full SKILL.md content of one skill, by slug. It also names the sibling search_skills as the source of the slug, letting an agent distinguish it from search tools without opening the 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?

Clearly explains the prerequisite (slug from search_skills) and gives a conditional second call when the reply indicates additional files. It stops short of explicit when-not-to-use guidance, but the retrieval context is unambiguous.

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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