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djerok

GLM Subagent MCP

Run GLM as a file-accessing agent

glm_agent
Destructive

Run repository coding tasks end-to-end using GLM as an agent with file tools. Provides a concise summary and stats; use dry_run to preview changes before applying.

Instructions

Run GLM as a real coding agent with its OWN file tools (read/write/edit/list/bash); it works your repo end-to-end on GLM tokens (~10x cheaper than Opus). Prefer this over doing repo work yourself. Pass task + absolute workdir. Returns a concise summary+stats (use dry_run to preview a diff first). Not for sensitive, huge-context, or heavy dependent-tool-loop work.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe coding task for GLM to carry out end-to-end in the repo.
modelNoModel id or 'auto' (default, peak-aware).
formatNoconcise (default: summary+stats+changed files) or detailed (adds full diff). dry_run always shows the diff.
contextNoOptional extra context/constraints (GLM can also read files itself).
dry_runNoIf true, GLM proposes a diff and writes nothing (preview before applying). Default false.
workdirNoAbsolute path to the project root GLM should operate in. Defaults to the server's cwd; always pass it explicitly.
thinkingNoEnable GLM reasoning mode for harder tasks. Default false.
max_tokensNoMax output tokens per turn (ceiling; billed for actual). Default generous.
Behavior4/5

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

Annotations already declare destructiveHint=true and openWorldHint=true, so the description adds context about cost (~10x cheaper than Opus) and the scope of file tools (read/write/edit/list/bash). It does not reiterate the destructive nature but adds complementary details. No contradiction with 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?

Three sentences, each serving a distinct purpose: what the tool does, when to use it, and return value format. Front-loaded with the core capability, no redundant words. Highly efficient.

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?

Given the tool's complexity (8 params, destructive, no output schema), the description covers purpose, usage guidelines, behavioral traits (cost, file tools), return value (summary+stats), and limitations. It is fully adequate for informed selection.

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 description coverage is 100%, so baseline is 3. The description adds meaning beyond schema by emphasizing 'Pass task + absolute workdir', explaining dry_run preview behavior, and noting default model is 'auto' (peak-aware). This adds moderate value.

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 clearly states the verb 'Run' and resource 'GLM as a real coding agent with its own file tools', specifying it works the repo end-to-end. It distinguishes from siblings (glm_delegate, etc.) by highlighting the agent's file-accessing capabilities and cost efficiency.

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

Usage Guidelines5/5

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

Explicitly advises 'Prefer this over doing repo work yourself' and lists exclusions: 'Not for sensitive, huge-context, or heavy dependent-tool-loop work.' This provides clear when-to-use and when-not-to-use guidance, with implied alternatives via sibling tools.

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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