Skip to main content
Glama

Get a Depot CI job step summary

depot_get_ci_job_summary
Read-onlyIdempotent

Retrieve the human-authored markdown summary a CI job published for itself, including test results and coverage deltas, to understand job outcomes at a glance.

Instructions

Read the step summary a Depot CI job authored for itself — the equivalent of GitHub Actions' $GITHUB_STEP_SUMMARY.

This is markdown the job's own steps chose to publish: test result tables, coverage deltas, lint counts, deployment URLs. When a job writes one, it is usually a far better explanation of what happened than its logs, because a human decided what mattered.

Most jobs write nothing here, and an empty result is normal rather than an error. If it comes back empty, use depot_diagnose_ci_failure for a failure, or depot_get_ci_logs for raw output.

"id" accepts an attempt id, a job id, or a run id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesAn attempt id, job id, or run id.
targetTypeNoWhat "id" refers to. Omit to let the server work it out.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
emptyYes
targetYes
markdownYes
truncatedYes
contentWarningYesReminder that the markdown was authored by the job itself and is unverified.
originalLengthYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool readOnly, idempotent, and non-destructive; the description adds meaningful behavior beyond that: the summary is job-authored markdown and 'an empty result is normal rather than an error.' This prevents misinterpreting empty output as failure and clarifies the accepted id forms (attempt, job, or run id). No contradiction with annotations exists.

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?

Every paragraph earns its place: purpose, interpretation/value, empty-result handling with sibling routing, and id flexibility. The first sentence is front-loaded with the exact operation, and there is no redundant filler.

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?

For a tool with 100% schema coverage, an output schema, and strong annotations, the description covers purpose, usage context, empty behavior, alternatives, and id semantics. An agent has enough information to call the tool correctly and interpret the result without further inference.

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?

Input schema coverage is 100%, including an enum for targetType and a clear description for id. The description mostly restates the id flexibility without adding syntax or format details beyond the schema. Baseline 3 is appropriate because the schema carries the parameter-semantics weight.

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 opening sentence names a specific verb ('Read'), a concrete resource ('step summary a Depot CI job authored for itself'), and anchors it to a familiar GitHub Actions concept ($GITHUB_STEP_SUMMARY). This clearly differentiates the tool from log- or run-level tools like depot_get_ci_logs or depot_get_ci_run even before reading 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 Guidelines5/5

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

The description explicitly tells the agent that an empty result is normal and should not be treated as an error. It then routes to siblings: use depot_diagnose_ci_failure for a failure and depot_get_ci_logs for raw output, which is clear when-to-use vs alternatives guidance. It also explains why the summary is preferable to logs when present.

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

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/akshayjain3450/depot-mcp'

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