Skip to main content
Glama

claude

Destructive

Execute complex coding tasks for implementation, refactoring, and code review via Anthropic's agent. Runs non-interactively in your directory, edits files, and returns actionable errors.

Instructions

Claude Code — Anthropic's coding agent for complex implementation, refactoring, and code review. Runs the claude CLI non-interactively in cwd — it can read and edit files there and may take time or use the agent's own model quota — and returns its output. On common failures returns a classified, actionable error (not installed / not authenticated with the exact login command / not configured / timed out / busy / output-limit); other non-zero exits return a clipped stderr/stdout tail. Check availability with list_agents first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoWorking directory for the CLI. Prefer an absolute path; a relative path resolves from the server process's cwd. Not a sandbox — the agent may read/edit any files it can access.
modelNoOptional model id passed through to the CLI, overriding that CLI's configured/default model (e.g. "o3"). Model names are agent-specific. Must match [A-Za-z0-9][A-Za-z0-9._:/-]{0,127} — a flag-shaped value is rejected.
promptYesThe task or question for the agent, in natural language.
timeoutMsNoTotal runtime cap in milliseconds — the hard upper bound on the whole run once the CLI starts (excludes time queued behind the concurrency limit); the process group is killed if exceeded (default 1800000 = 30 minutes).
idleTimeoutMsNoIdle/inactivity timeout in milliseconds — the run is killed only if the agent produces NO output for this long (the timer resets on each output chunk; default 300000 = 5 minutes).
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=true), the description discloses the non-interactive execution, file read/edit capabilities, potential time and quota usage, and a detailed error-handling scheme (classified errors for common failures, clipped tail for others). This significantly enriches the agent's understanding of side effects and limitations.

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 four sentences, each carrying distinct information: what the tool is, how it executes, how failures are handled, and a prerequisite. There is no redundancy, and the most important facts are front-loaded.

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

Completeness4/5

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

The description covers purpose, execution, error behavior, and prerequisites, which is quite complete for a complex tool with no output schema. It briefly explains the return value ('returns its output' and failure modes), though a more detailed description of the successful output format would make it fully complete.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description mentions 'cwd' contextually but does not add semantics beyond what the schema already provides; the schema itself fully describes parameters like timeoutMs, model, and prompt.

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 tool's function: 'Runs the `claude` CLI non-interactively in `cwd`' for 'complex implementation, refactoring, and code review'. This specifies both the verb and resource, and identifies it as 'Anthropic's coding agent', distinguishing it from sibling tools like codex or run_all.

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 clear context for when to use: for complex coding tasks requiring implementation, refactoring, or code review. It also gives a specific prerequisite ('Check availability with list_agents first') and warns about time and model quota costs, which helps the agent decide. However, it does not explicitly contrast with alternatives like codex, so it lacks explicit exclusions.

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/blackaxgit/agent-mcp-hub'

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