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read_process_output

Read-only

Retrieve new stdout and stderr output from a running background process by specifying offsets and wait time. Read captured output to monitor progress or diagnose issues.

Instructions

Read captured stdout and stderr from a managed background process. Pass the previous "next_stdout_offset" and "next_stderr_offset" values as "since_stdout" and "since_stderr" to receive only new output, and "wait_ms" to block until output arrives or the process exits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pidYesProcess ID returned by start_process.
wait_msNoWait up to this long for new output or process exit.
process_idNoOpaque processId returned by start_process; detects stale PID references.
since_stderrNoReturn stderr produced after this offset.
since_stdoutNoReturn stdout produced after this offset.

Schema Changelog

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

  1. Changed1 schema field changedv1.2.1
    • addedInput schema / properties / process_id
      Added value: +{
      +  "description": "Opaque processId returned by start_process; detects stale PID references.",
      +  "type": "string"
      +}
  2. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral detail: that passing since_stdout/since_stderr yields only new output and that wait_ms blocks until output arrives or the process exits. This goes beyond schema and 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?

Two sentences with no filler. The first sentence states the core function; the second explains the key usage pattern. Information is front-loaded and each clause earns its place.

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 the main behavioral loop: read, get offsets, pass them back, optionally wait. It also indirectly signals the response contains next_stdout_offset and next_stderr_offset. Given no output schema, this is sufficient for an agent to invoke and interpret results 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. The description adds value by explaining where the offset parameters come from ('previous next_stdout_offset and next_stderr_offset values') and clarifying the effect of wait_ms. This enriches the schema's minimal per-parameter descriptions.

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 opens with a specific verb and resource: 'Read captured stdout and stderr from a managed background process.' This clearly distinguishes it from siblings like process_status, process_wait, and process_write, and makes the tool's role immediately obvious.

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?

The description gives clear usage context: how to use offsets for incremental reads and wait_ms for blocking behavior. It doesn't explicitly mention alternatives or when not to use it, but the context is concrete enough that an agent can decide when to invoke it.

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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